developing an adaptive model of thermal comfort …...i developing an adaptive model of thermal...

i

Developing an Adaptive Model of Thermal Comfort and Preference

FINAL REPORT

ASHRAE RP- 884

March 1997

Richard de DearÀ, Gail BragerÁ, Donna CooperÀ

À Macquarie Research Ltd., Macquarie University, Sydney, NSW 2109 AUSTRALIA

Á Center for Environmental Design Research, University of California,

Berkeley, CA 94720 USA

“Results of Cooperative Research between the American Society of Heating, Refrigerating

and Air Conditioning Engineers, Inc., and Macquarie Research, Ltd.”

iii

i

TABLE OF CONTENTS iii

ACKNOWLEDGMENTS vii

EXECUTIVE SUMMARY ix

CHAPTER 1 - INTRODUCTION & BACKGROUND 1

1.1. Introduction 1

1.2. Defining the adaptive process 3 1.2.1. The dialectic of contemporary thermal comfort theory 3 1.2.2. The “adaptive” hypothesis 4

1.3. A conceptual model of adaptation -- feedback loops 6 1.3.1. Behavioral feedback - adjustment 8 1.3.2. Physiological feedback -- acclimatization 10 1.3.3. Psychological feedback -- habituation and expectation 12

1.4. Literature review 13 1.4.1. Climate chamber evidence for adaptation to climate 13 1.4.2. Field evidence for adaptation 15

1.4.2.1. The earlier field evidence for adaptation 16 1.4.2.2. Analysis of neutral temperatures using recent field experiments 18 1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment 22 1.4.2.4. Evidence for psychological adaptation - expectation and context 23

1.5. Implications for RP-884 26 1.5.1. Lessons from static heat balance models 26 1.5.2. Time scales of thermal adaptation 29

1.6. Aims 31

CHAPTER 2 - METHODS 33

2.1. Overview of the RP-884 approach 33

2.2. Establishing the database for RP-884 36 2.2.1. Sourcing the raw data 36 2.2.2. Ratings of raw data submitted to RP-884 40

2.3. Raw data standardisation 41 2.3.1. Creation of a standard data template 41 2.3.2. Consistent mean radiant temperatures within the database. 42 2.3.3. Consistent comfort index calculations within the database 42 2.3.4. Predicted draft risk index (PD) 43 2.3.5. Clothing insulation in the ASHRAE RP-884 database 44

2.3.5.1. Discrepancies between field estimation methods for clo. 45 2.3.5.2. The chair insulation effect 49

2.4. Developing an index for perceived thermal control 49

iv

2.5. Thermal acceptability issues within the RP-884 database 51 2.5.1. Developing a proxy variable for thermal acceptability based on thermal

sensation votes. 51 2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55

acceptable indoor climate guidelines 52

2.6. Outdoor meteorological/climatological data for the data base 52 2.6.1. Appending outdoor weather observations to each row of data 52 2.6.2. Climate classification applied to RP-884 raw data 53

2.7. Subdivision of the standardized field experiments 54

2.8. The meta-analysis 54 2.8.1. The unit of analysis for the RP-884 meta-analysis 54 2.8.2. Meta-file’s structure and coding conventions 55 2.8.3. General assumptions within the statistical meta-analysis 55 2.8.4. Statistical treatments on the various subjective thermal ratings 56 2.8.5. Preferred temperatures 59

2.9. The RP-884 database in the public domain and disseminated via the world wide web 60

2.10. Summary of the methods used in RP-884 64

CHAPTER 3 - BASIC RESULTS 67

3.1. Interactions with indoor climate 67 3.1.1. Thermal sensation 67

3.1.1.1. Dependence of thermal sensation on indoor operative temperature 68 3.1.1.2. Dependence of thermal sensation on indoor ET 69 3.1.1.3. Dependence of thermal sensation on PMV 70 3.1.1.4. Dependence of thermal sensation on indoor SET 71

3.1.2. Thermal neutrality 72 3.1.2.1. Neutral operative temperatures (neut_top) 72 3.1.2.2. Neutral effective temperatures (neut_et) 74 3.1.2.3. Neutral predicted mean votes (neut_pmv) 74 3.1.2.4. Predicted neutralities with the PMV heat balance model 75 3.1.2.5. Neutral standard effective temperatures (neut_set) 77

3.1.3. Thermal acceptability and indoor climate 78 3.1.3.1. Relationship between direct and inferred thermal acceptability 78 3.1.3.2. Directly determined thermal acceptability 80 3.1.3.3. Thermal acceptability inferred from thermal sensation 83 3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures. 84

3.1.4. Thermal preferences and indoor climate 89 3.1.5. Comparisons between neutral and preferred temperatures indoors. 91 3.1.6. Behavioural adjustments to indoor climate 93

3.1.6.1. Thermal insulation adjustments indoors 94 3.1.6.2. Metabolic rate adjustments indoors 97 3.1.6.3. Air speed adjustments indoors 99

v

3.2. Interactions with outdoor weather and climate 102 3.2.1. Thermal neutrality and outdoor climate 102

3.2.1.1. Seasonal comparisons 103 3.2.1.2. Dependence of observed neutrality on outdoor climate 104 3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature 106

3.2.2. Thermal acceptability and outdoor climate 108 3.2.3. Thermal preference and outdoor climate 110 3.2.4. Behavioral responses to outdoor climate 113

3.2.4.1. Indoor clothing and outdoor climate 114 3.2.4.2. Metabolic rate indoors related to outdoor climate 115 3.2.4.3. Indoor air speeds in relation to outdoor climate 116

3.3. Influence of building characteristics on thermal comfort 118 3.3.1. HVAC versus natural ventilation 118

3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings 119

3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings 121 3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings. 122

3.3.2. Personal environmental control 124 3.3.3. Building occupancy types - offices, residential and industrial 127

3.4. Summary of basic results 130 3.4.1. Summary of thermal sensation, acceptability and preference 131 3.4.2. Summary of thermal sensitivity and behavioral thermoregulation 133 3.4.3. Summary of the effects of outdoor climate on thermal perception indoors 134 3.4.4. Summary of the effects of contextual factors and perceived control 135

CHAPTER 4 - TOWARDS ADAPTIVE MODELS 139

4.1. The semantics of thermal comfort 139

4.2. Comparison of RP-884 models with earlier adaptive model publications 141

4.3. Comparison of RP-884 models with the PMV “static model” 145 4.3.1. Comparisons within the centrally conditioned building sample 146 4.3.2. Comparisons within the naturally ventilated building sample 150

4.4. Adaptive models for acceptable ranges of indoor temperatures 152

CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS 155

5.1. A variable temperature standard for application in buildings with centrally controlled HVAC 155

5.1.1. Purpose 155 5.1.2. Scope 156 5.1.3. Definitions 156 5.1.4. Conditions for an acceptable thermal environment. 161

5.1.4.1. Analytic PMV method 161 5.1.4.2. Adaptive PMV method 161 5.1.4.3. Prescriptive method 163

5.2. A variable temperature standard for application in naturaly ventilated buildings 165

vi

5.2.1. Purpose 165 5.2.2. Scope 165 5.2.3. Definitions 166 5.2.4. Conditions for an acceptable thermal environment. 168

BIBLIOGRAPHY 171

APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE 185

APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE 227

APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE 235

C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702 236

C.2. Project Title - Thermal comfort studies in modern industrial buildings. 239

C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: An integrated assessment of electricity conservation in Thailand’s commercial sector. 242

C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer Technology Test (ACT2) project. 245

C.5. Project Title - Higher PMV causes higher energy consumption in air- conditioned buildings: a case study in Jakarta, Indonesia. 248

C.6. Project Title - Montreal ASHRAE RP-821. 250

C.7. Project Title - Richard de Dear’s PhD research project in Australia. 253

C.8. Project Title - A field study of thermal comfort using questionnaire software. 256

C.9. Project Title - “Thermal comfort in Pakistan.” 258

C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL task. 262

C.11. Project Title - Developing indoor temperatures for naturally ventilated buildings. 264

C.12. Project Title - Mixed mode climate control: some hands-on experience. 267

C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area. 269

C.14. Project Title - A field investigation of thermal comfort environmental satisfaction and perceived control levels in UK office buildings, University of Liverpool. 272

C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air conditioned and naturally ventilated buildings in Singapore. 275

C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US 277

C.17. Project Title - Sunset building: a study of occupant thermal comfort in support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum energy efficiency 279

C.18. Project Title - The Verifone building, a component of the Advanced Customer Technology Test (ACT2) Project. 282

APPENDIX D - CLIMATE CLASSIFICATION 285

APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE 287

vii

APPENDIX F - CODEBOOK FOR THE RP-884 META-ANALYSIS 291

APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE 295

ACKNOWLEDGMENTS

The successful completion of this project depended very heavily on the willingness of field

researchers to make available their raw data for re-analysis and incorporation into the RP-

884 database. In particular, we would like to thank the following contributors:

Dr. Jill Brown, formerly of University of Wales, Cardiff; Dr. John Busch Jr. Lawrence

Berkeley Labs., California; Prof. Cris Benton, CEDR, University of California at Berkeley;

Dr. Tri Karyono, Agency for the Assessment and Application of Technology (BPPT),

Jakarta, Indonesia (formerly of the Department of Architecture, University of Sheffield, UK);

Dr. Giovanna Donnini, formerly of Auger, Donnini and Nguyen Inc, Montreal, Canada; Dr.

Guy Newsham, Institute for Research in Construction, National Research Council of

Canada, Ottawa; Fergus Nicol, School of Architecture, Oxford-Brookes University, UK.;

Iftikhar Raja, School of Architecture, Oxford-Brookes University, UK; Prof. Nick Baker, The

Martin Centre for Architecture and Urban Studies, University of Cambridge, UK; David

Rowe, Dept. of Architectural and Design Science, University of Sydney, Australia; Dr Ruth

Williams, The Building Services Research and Information Association, UK (formerly

Liverpool University, UK); Fred Bauman, CEDR, University of California at Berkeley.

RP-884 also depended on weather and climate data resources. Such data was required for

the relevant sites and periods covered by field experiments within the database. Apart from

resources available on the WWW and various CD-ROM publications, the following

organisations provided data. The Australian Bureau of Meteorology’s National Climate

Centre supplied meteorological data for the Melbourne, Brisbane and Darwin field

experiments. The Oxford University Radcliffe Observatory supplied meteorological

observations for some of the UK experiments. Macquarie University’s Meteorological Site

supplied observations for the Sydney field data. The US National Climate Data Center

(NCDC) supplied meteorological data for the Californian experiments. Meteorological

viii

observations for Grand Rapids were supplied by the Michigan State Climatologist.

Bangkok meteorological data were supplied by the Royal Thai Meteorological Department.

Special thanks are also due to Andris Auliciems of the University of Queensland, Fergus

Nicol of Oxford-Brookes University and Michael Humphreys of Oxford University for their

pioneering work in the area of adaptive models and also for their encouragement at various

stages during the ASHRAE RP-884 project.

ix

EXECUTIVE SUMMARY

One of the more contentious theoretical issues in the applied research area of thermal

comfort has been the dialectic between “adaptive” and “static” models. Apart from having

disparate methodological bases (the former laboratory-experimental, the latter field-based),

the two approaches have yielded starkly differing prescriptions for how the indoor climate of

buildings should be managed. These prescriptions carry implications for the types of

permissible building designs, the means by which their thermal environments are controlled,

and the amounts of energy they consume in the production of habitable indoor climates.

Static models have led to indoor climate standards that have been universally applied

across all building types, are characterised by minimal recognition of outdoor climatic

context, and are contributing to an increased reliance on mechanical cooling. In contrast,

proponents of adaptive models have advocated variable indoor temperature standards that

more fully exercise the adaptive capabilities of building occupants. This approach

potentially leads to more responsive environmental control algorithms, enhanced levels of

occupant comfort, reduced energy consumption, and the encouragement of climatically

responsive building design.

Despite these apparent differences, our review of the research literature emerging from both

approaches indicated that this seemingly irreconcilable split was primarily the result of

narrow definitions of the term “thermal adaptation”, and that there were opportunities to

bridge some of the gap between the hypotheses. We suggest that human thermal

adaptation is comprised of three distinct yet interrelated processes - behavioral,

physiological, and psychological. The adoption of this tripartite definition goes some way

towards reconciling the static and adaptive approaches and the indoor climate standards

derived from them.

This project’s principal objective was the proposal of a variable temperature standard based

on the adaptive approach. Where it differs from earlier attempts is in the quality control

applied throughout its adaptive modelling method. About 21,000 sets of raw thermal

comfort data from 160 buildings were collected from most of the thermal comfort field

research groups around the world who are currently active. Data selection criteria

x

emphasized precision of indoor climatic instruments, while data assimilation involved a

variety of questionnaire standardization processes. For example, each one of the over

21,000 building subjects’ clothing thermal insulation estimates was transformed into an

equivalent clo value using consistent procedures specified in ASHRAE Standard 55-1992.

The thermal effects of chairs for seated subjects was also included. For each set of raw

data, outdoor meteorological and climatological data were appended to the RP-884

database. All indoor and outdoor thermal indices were recalculated using a standard

software package (WinComf©) recently commissioned by ASHRAE’s TC 2.1. Since a

significant component of this project’s effort was expended in the assembly of the database,

and since that database has relevance to thermal comfort research problems extending well

beyond the scope of RP-884, we have chosen to place this valuable data resource in the

public domain (World Wide Web) where it can be used by the international thermal comfort

research community.

After statistically analysing the raw data collected in each of the RP-884 database’s 160

buildings, we conducted a meta-analysis of human subjective response to indoor climate

and how it interacted with indoor architectural, contextual and outdoor meteorological

factors. The main subjective response variables were thermal neutrality (derived from

thermal sensation votes) and preferred temperature. Eighty and 90% thermal acceptability

criteria for general thermal comfort were estimated for each building as the range of

operative temperatures falling between mean thermal sensations of ±0.85 and ±0.5

respectively. The list of independent variables in the meta-analysis included the following

indoor climatic indices: operative temperature, effective temperature, PMV/PPD and

standard effective temperature. Outdoor climate was operationalized as an independent

variable in our meta-analysis as the mean of daily minimum and maximum outdoor effective

temperatures prevailing during each building’s survey period. The most important contextual

factor in our meta-analysis was a classification of buildings as having either central HVAC or

being naturally ventilated. This distinction was a unique feature of the ASHRAE RP-884

project, and produced some of the most significant results.

The meta-analysis clearly indicated that the definition and prescription for thermal

acceptability contained in ASHRAE Standard 55-92 bore little resemblance or relationship

to the levels actually expressed by occupants within the building sample. Thermal sensation

xi

and thermal preference on the other hand, demonstrated statistically significant dependence

on indoor thermal indices prevailing at the time of the questionnaire (these included

operative, effective and standard effective temperatures, or PMV/PPD). Thermal neutrality,

defined as the operative temperature most closely corresponding with a mean thermal

sensation vote of zero (“neutral”) showed an adaptive relationship with mean indoor

temperatures - warm buildings had warm neutralities and vice versa. However, this adaptive

relationship was stronger in naturally ventilated buildings than in buildings with centralized

HVAC systems. Similar adaptive relationships were established for neutrality and

preference with outdoor climate, and again, the strength of the relationship was greater in

the sample of naturally ventilated buildings. These observations support the notion that

building occupants’ thermal ideals are influenced by their thermal experiences both indoors

and outdoors.

Preferred temperature for a particular building did not necessarily coincide with thermal

neutrality, and this semantic discrepancy was most evident in HVAC buildings where

preference was depressed below neutrality in warm climates and elevated above neutrality

in cold climates (i.e, people preferred to feel cooler than neutral in warm climates, and

warmer than neutral in cold climates). This finding suggests that much of what has been

regarded as climatic adaptation by previous proponents of the adaptive model was in fact a

consequence of defining thermal optima in terms of neutrality instead of preference.

Clothing insulation worn by building occupants demonstrated a dependence on both mean

indoor and outdoor temperatures. Thermal insulation levels worn indoors decreased as

indoor and outdoor temperatures increased, while mean indoor air speed demonstrated a

positive dependence on prevailing temperature levels. The close agreement between PMV

model predictions of optimum indoor temperature and those actually observed within HVAC

buildings suggests that the type of thermal adaptation found in such buildings was of the

behavioral type, mainly driven by adjustments to clothing and indoor air speed. In contrast,

the range of optimum indoor temperatures observed in naturally ventilated buildings was

about twice as large as that predicted by the PMV model, suggesting that physiological

(acclimatisation) and psychological (shifting expectations) adaptive processes were

superimposed on the behavioral adaptations of clothing and air speed adjustment in the

naturally ventilated context.

xii

Based on these adaptive relationships between indoor comfort and outdoor climate, the RP-

884 project concluded with a pair of variable temperature standards. One standard was

designed for use in HVAC buildings where occupants had little or no adaptive opportunity,

while the other was designed for naturally ventilated buildings where occupants had access

to operable windows and other adaptive opportunities. The HVAC standard was based on

three alternative methods; a) the analytic PMV method for use whenever accurate estimates

for all the heat-balance model’s inputs were feasible; b) the modified “adaptive PMV”

method for use whenever an accurate estimate of mean outdoor effective temperature was

possible (defined as the arithmetic average of 6am and 3pm outdoor effective

temperatures), and c) the prescriptive method for use whenever the first two approaches

were not feasible (presented as summer and winter comfort zones on the psychrometric

chart). Acceptable ranges of operative temperature were applied symmetrically above and

below predicted optimum operative temperatures. The average winter prescription for 90%

general thermal acceptability (excluding local discomforts) was given as 22.5°C ± 1.2 K

while the summer prescription was given as 23.5°C ± 1.2 K.

The variable temperature standard for use in naturally ventilated buildings was given as an

adaptive linear regression model based on outdoor weather and climate:

optimum indoor temperature = 18.9 + 0.255 * (outdoor mean ET*)

Acceptable temperature ranges around the optimum in naturally ventilated buildings were

specified as ±3.5 for 80% general acceptability and ±2.5 for 90% general acceptability.

The RP-884 project leads to the conclusion that the PMV model represents a useful adjunct

to comfort standards intended for use exclusively within HVAC buildings where occupants

have little or no opportunity to adapt themselves, nor their immediate occupied zone.

However, application of this same model in naturally ventilated settings leads to significant

errors since it overlooks an important adaptive response in the form of variable thermal

expectations of building occupants in such buildings. In naturally ventilated settings we

recommend the application of an adaptive model that predicts optimum indoor temperature

from a knowledge of the building’s meteorologic or climatic setting.

ASHRAE RP-884 Final Report

Introduction & Background page MRL Australia 1

CHAPTER 1 - INTRODUCTION & BACKGROUND

1.1. Introduction

The way we design, construct, and operate buildings has profound implications for the

quality of both the natural and built environments. All too often today’s buildings require

massive resource inputs, create bleak or potentially unhealthy indoor environments,

pollute both their local and global environments through increased greenhouse

emissions, as well as contributing to the destruction of natural habitats (Barnett and

Browning 1995). The energy required to heat and cool our buildings, and the very way

we define the “comfortable” thermal conditions we are trying to maintain, play significant

roles in this environmental impact. The use of energy for heating, ventilating and air-

conditioning (HVAC) of the indoor environment is already the largest sector in energy

consumption in most of the developed world (Griffiths et al 1988). As well we seeing a

significant increase in HVAC energy use in developing and newly industrialized

countries as well (Ang 1986, Abro 1994). This is particularly relevant to the rapidly

developing tropical regions of the Asia-Pacific region, where traditional lifestyles in

naturally ventilated buildings are giving way to an increased reliance on mechanical

cooling. This in turn is changing both the way we design buildings and building

occupants’ expectations and behavioral patterns related to air conditioning (Lovins

1992).

It is commonly estimated that persons in economically developed countries spend at

least 80% of their time indoors. This suggests that the quality of the indoor environment

can have a significant impact on comfort, health, and overall sense of well-being. In an

effort to maintain the quality of the indoor environment, we mechanically condition our

buildings to provide constant, uniform, “comfortable” environments. The current

standards that define what those “comfortable” conditions should be were conducted

primarily with university students and in mid-latitude climate regions (ASHRAE 1992,

ISO 1994). Other than allowing for only a slight seasonal shift in the comfort zone based

on clothing adjustments, it is often suggested that the standards are universally

applicable across all building types, climates, and populations (Parsons 1994 and



discussion). A strict reliance on laboratory-based comfort standards also ignores

important cultural and social differences in the need or desire for air conditioning. A

special issue of Energy and Buildings (Kempton and Lutzenhiser 1992) focused on

these non-thermal issues, with a variety of papers examining how individuals and

cultures vary in their perceived need for and expectations of air conditioning.

But perhaps the single biggest issue in this debate remains the applicability of

standards in buildings which aren’t air conditioned at all. For example, when recently

asked by a union official whether or not Standard 55 (ASHRAE 1992) was applicable to

un-air-conditioned premises, ASHRAE’s Technical Committee (TC 2.1) responsible for

the standard openly declared that their comfort charts were intended for both HVAC and

naturally ventilated premises. Many researchers, however, challenge this assumption of

universal applicability, arguing that it ignores important contextual differences that can

attenuate responses to a given set of thermal conditions. While the “comfort zone” might

be viewed by the engineering community as a design goal for a deterministic HVAC

control system, its relevance to naturally ventilated buildings where conditions are

inherently much more variable is questionable (Forwood 1995). This was also

acknowledged by Givoni (1992), who revised his already notable work on the building

bioclimatic chart. He expanded the boundaries of the comfort zone based on the

expected indoor temperatures achievable with different passive design strategies,

applying a “common sense” notion that people living in unconditioned buildings become

accustomed to, and grow to accept higher temperature or humidities. Strict and literal

interpretation of the static “comfort zone” precludes application to anything other than

full-blown HVAC designs across the world’s moderate to extreme climate zones.

An alternative to traditional comfort theory - termed the “adaptive model” of comfort -

embraces the notion that people play an instrumental role in creating their own thermal

preferences. This is achieved either through the way they interact with the environment,

or modify their own behavior, or because contextual factors and past thermal history

change their expectations and thermal preferences. Interest and research into this

“adaptive” theory of thermal comfort first began in the mid-70’s in response to the oil-

shocks, and has recently regained momentum due to increasing concerns over human

impact on global climatic environment. There are numerous benefits to be gained from



an improved understanding of the influence of adaptation on thermal comfort in the built

environment. These include improved predictive models and standards, more

sophisticated and responsive environmental control algorithms, increased opportunities

for personal control, enhanced levels of thermal comfort and acceptability among

occupants, reduced energy consumption, and the encouragement of climatically

responsive and environmentally responsible building design.

This research project, “ASHRAE RP-884 - Developing an Adaptive Model of Thermal

Comfort and Preference”, is premised on the development and analysis of a quality-

controlled, cumulative database compiled from previous thermal comfort field

experiments worldwide. The aim is to use this database to refine our conceptual

understanding of adaptive mechanisms, to develop an empirical model of the adaptive

process, and to propose a variable temperature standard to supplement the current

ASHRAE Standard 55 (1992).

1.2. Defining the adaptive process

1.2.1. The dialectic of contemporary thermal comfort theory

In contemporary thermal comfort research, there is a perceived irreconcilable split into

“static” and “adaptive” schools of thought (Auliciems 1989; Nicol 1993). In the “static”

camp are ASHRAE’s Standard 55 --Thermal Environmental Conditions for Human

Occupancy (ASHRAE 1992) and the ISO Standard 7730 (ISO 1994). The static model

essentially views the person as a passive recipient of thermal stimuli. It is premised on

the assumption that the effects of a given thermal environment are mediated exclusively

by the physics of heat and mass exchanges at the surface of the body, while the

maintenance of a constant internal body temperature necessitates some physiological

responses. It is generally assumed in the static school of thought that thermal

sensations (hot-warm-cool-cold) are proportional to the magnitude of these

physiological responses, as measured by mean skin temperature and latent heat loss or

wettedness due to sweating (Benzinger 1979). The deterministic logic underpinning

heat balance comfort models such as PMV, ET* and SET* is:

physics ⇒ physiology ⇒ subjective discomfort



These models are based on extensive and rigorous laboratory experiments, and yield

fairly consistent, reproducible results in climate chambers. However, researchers are

increasingly exploring the extent to which we can directly apply these laboratory-derived

models, without modification, to the task of predicting subjective responses to thermal

conditions in real buildings, where the interactions between the occupants and indoor

climate are exceedingly complex. Adherents to the adaptive school of thought regard

the simplistic cause-and-effect approach embodied in the static models as inadequate

to describe thermal perception in the real world. As such the static hypothesis has

come to be regarded as a “single temperature” model of thermal comfort (Humphreys

1981, 1994a, Nicol 1993: Auliciems 1989). But a more conciliatory interpretation of the

heat balance model depicts it as partially adaptive, since it does include the impact of

thermal variables and clothing which can be adjusted by the occupant.

1.2.2. The “adaptive” hypothesis

With the static heat-balance models representing one side, on the other side of this

dialectic is the “adaptive” school of thought in which factors beyond the fundamental

physics and physiology all interact with thermal perception. These factors can include

demographics (gender, age, economic status), context (building design, building

function, season, climate, semantics, social conditioning), and cognition (attitude,

preference, and expectations) (McIntyre 1982, Baker 1993, Baker and Standeven 1994,

Oseland 1994a,b, Griffiths et al 1988). These factors have been demonstrated time

and again to be irrelevant to the comfort responses of subjects in the contrived setting of

the climate chamber (Fanger 1972b, de Dear et al 1991a). However, there remains a

lingering suspicion in the minds of adaptive modellers and practitioners alike that such

considerations cannot be dismissed so easily in the context of real buildings.

The generic term “adaptation” might broadly be interpreted as the gradual diminution of

the organism’s response to repeated environmental stimulation. As used in RP-884,

adaptation subsumes all physiological mechanisms of acclimatization, plus all

behavioral and psychological processes which building occupants undergo in order to

improve the “fit” of the indoor climate to their personal or collective requirements. Within



this broad definition it is possible to clearly distinguish three categories of adaptation

(Folk 1974, 1981, Goldsmith 1974, Prosser 1958, Clark and Edholm 1985):

1. Behavioral Adjustment. This includes all modifications a person might consciously,

or unconsciously make, which in turn modify heat and mass fluxes governing the body’s

thermal balance. We define adjustment in terms of three subcategories:

a) Personal adjustment: adjusting to the surroundings by changing personal

variables, such as adjusting clothing, activity, posture, eating/drinking hot/ cold

food or beverages, or moving to a different location;

b) Technological or environmental adjustment: modifying the surroundings

themselves, when control is available, such as opening/closing windows or shades,

turning on fans or heating, blocking air diffusers, or operating other HVAC

controls, etc.; and

c) Cultural adjustments, including scheduling activities, siestas, dress codes

2. Physiological. The most comprehensive definition of physiological adaptation

would include all of the changes in the physiological responses which result from

exposure to thermal environmental factors, and which lead to a gradual diminution in the

strain induced by such exposure. Physiological adaptation can be broken down into at

least two subcategories:

a) Genetic adaptation: alterations which have become part of the genetic

heritage of an individual or group of people, but developing at time

scales beyond that of an individual’s lifetime, and

b) Acclimation or Acclimatization (used interchangeably here): changes in the

settings of the physiological thermoregulation system over a period of days

or weeks, in response to exposure to single or a combination of thermal

environmental stressors.

3. Psychological. The psychological dimension of adaptation to indoor climate refers

to an altered perception of, and reaction to, sensory information. Thermal perceptions

are directly and significantly attenuated by one’s experiences and expectations of the

indoor climate. This form of adaptation involves building occupants’ “comfort setpoints”

which may vary across time and space. Relaxation of indoor climatic expectations can



be likened to the notion of habituation in psychophysics -- repeated or chronic exposure

to an environmental stressor leading to a diminution of the evoked sensation’s intensity

(Glaser 1966, Frisancho 1981).

habituationpsychological adaptation -

changing expectations

adjustmentbehavioral/technologicalchanges to heat-balance

Adaptation toIndoor Climate

acclimatizationlong-term physiological

adaptation to climate

Figure 1.1: The three components of adaptation to indoor climate

1.3. A conceptual model of adaptation -- feedback loops

An important premise of the adaptive model is that the building occupant is no longer

simply a passive recipient of the thermal environment as given, as in the case of a

climate chamber experimental subject, but instead is an active agent interacting with all

levels of the person-environment system via feedback loops. We continue to

emphasize, however, our opinion that this perspective complements rather than

contradicts the “static” heat-balance view as outlined above. The heat-balance model

does partially account for adaptation by using as inputs those parameters affected by

adjustment and environmental interventions, but it explicitly rules out any notions of

physiological and psychological adaptation.

In contrast, the adaptive model draws upon a phenomenological perspective that

emphasizes how people interact with and change their environment, and accounts for

the ways in which a person’s past experience, future plans, and intentions influence

one’s perception (Canter 1983, Wohlwill 1974, Helson 1964, Veitch and Arkkelin 1995,

Kaplan and Kaplan 1982). The adaptive hypothesis indicates that one’s satisfaction

with an indoor climate is achieved by a correct matching between the actual thermal



environmental conditions prevailing at that point in time and space, and one’s thermal

expectations of what the indoor climate should be like. Thermal expectations result from

a confluence of current and past thermal experiences, cultural and technical practices

(Auliciems 1981, 1989, de Dear 1993, Nicol 1993). These relationships have been

described in Figure 1.2, a schematic diagram developed by Auliciems (1981, 1989)

showing that a given set of indoor climatic conditions can elicit varying levels of comfort

and satisfaction from building occupants, depending on culture or climatic and

HVAC/architectural expectations.

Figure 1.2: The "adaptive model" of thermal perception (after Auliciems, 1981)

By logical extension, the adaptive hypothesis also implies that the temperatures people

expect indoors for comfort and satisfaction will move in the direction of the average

conditions encountered in their day-to-day life, both indoors and out. So, in the systems

schematic in Figure 1.2, outdoor climate acts as a negative feedback which attracts the

thermal perceptual sub-system’s set point, thereby damping load error



(dissatisfaction/discomfort) within the human behavioral thermoregulatory system. The

net result is that adapted building occupants may be perfectly comfortable at

temperatures beyond those recommended in standards such as ASHRAE 55 (1992)

and ISO 7730 (1984, 1994).

We believe that the development of an adaptive predictive model of thermal comfort

should combine features of both the static and adaptive theories, and that these various

feedback loops should be described in terms of how they affect the more traditional

linear relationships. As set out in the heat balance models

(physics ⇒ physiology ⇒ subjective discomfort)

1.3.1. Behavioral feedback - adjustment

Behavioral adjustment of the body’s heat-balance probably offers the greatest

opportunity for people to play an active role in maintaining their own comfort. The extent

to which building occupants can, or do, behaviorally interact with their indoor climate

depends a great deal on contextual factors. This is very important in both the

development and application of an adaptive model, and deserves further elaboration.

Context can be described in terms of adaptive opportunity, compared to the constraints

or restrictions on thermoregulatory degrees of freedom (Nicol and Humphreys 1972).

That is, “adaptive opportunity” refers to whether or not buildings afford their occupants

scope for adaptive interventions (Baker and Standeven 1994). This may result from:

a) an attribute of the building itself (e.g. are windows operable? how far are

occupants placed away from such windows? is the floor plan individual office

cells or open-plan bureau landschaft?),

b) characteristics of the active, or energy consuming, climate services inside

the structure (e.g. centralized HVAC services, or decentralized task

conditioning controls at each workstation?), or

c) the organizational and social conditions prevailing within the building (e.g. is

there a strict or casual dress code? are employees bound to a single

workstation for the entire working day?).



The flip-side of adaptive opportunity (i.e, the lack of...), is the analysis of constraints to

thermal control. These constraints may be gathered under five main headings (Nicol

and Humphreys 1972, Humphreys 1994a):

a) Constraints due to climate. Buildings in harsh or extreme climates might

present a more exclusive barrier to the elements than buildings in milder

climate, affording their occupants fewer adaptive opportunities.

b) Economic constraints. The costs of thermal environmental control, both

initial and recurrent, often exceed the resources of many countries.

c) Constraints due to social custom or regulation. To what extent can an

individual change his/her clothing? Are clothing patterns determined by

climate, fashion or religion? To what extent do the various requirements put

on us by other people, government energy guidelines, greenhouse gas

emission quotas or targets limit our freedom to behaviorally thermoregulate?

d) Constraints due to task or occupation. Often the requirements of a particular

job override those of thermal comfort, when there are formal dress codes of

fixed work locations.

e) Constraints due to design. This refers to design of the building or HVAC

system, availability of task-conditioning or personal environmental controls,

design quality of awnings, climatic suitability of window placement and size.

The concept of adaptive opportunity helps to differentiate those buildings in which a

deterministic relationship between the thermal environment and human response is

applicable, and those in which an adaptive feedback loop is fully operational. Adaptive

opportunity can be thought of as a continuum. At one extreme is the climate chamber in

which subjects are instructed what to wear and what activities they are to perform while

an external agent, the researcher, determines the temperature, humidity and air flow

regime they are to experience for the duration of the experiment. At the other extreme

we find the single-occupant room in which clothing and activity patterns are discretionary



and environmental controls cover the full range of possibilities from operable windows

through to task-ambient air conditioning.

The ultimate efficacy of any form of adaptive control must be measured in terms of

occupant satisfaction and ideally should be evaluated in terms of available control

(adaptive opportunity) vs. exercised control (actual physical control that takes place) vs.

perceived control (Paciuk 1989, 1990). But regardless of whether it is placebo or real

control, there seems little dispute in the literature that the issue of personal and

environmental control is central to thermal acceptability, and therefore should be a factor

examined in the RP-884 data analysis.

Behavioral adjustment represents the most immediate feedback link to the thermal

environment. Stated simply, if a person is uncomfortable, or expects to become so, they

are to take corrective action. What might have previously been regarded as the final

consequence in the static heat balance model (the conscious sensation of thermal

discomfort), becomes the starting point for this feedback in the adaptive model.

indoor clothing body’s physiol. thermal discomfort climate + activity heat load regulation sensation dissatisfaction Behavioral Adjustment

Figure 1.3: Behavioral feedback loop

1.3.2. Physiological Feedback -- acclimatization

Physiological acclimatization to cold stress is primarily associated with maintenance of

warmer skin temperatures and increased heat production, although it is not clear to what

extent the increased metabolic rate can occur without shivering (Frisancho, 1981).

Otherwise, adaptation to the cold is primarily behavioral (Clark and Edholm 1985). The

evidence for physiological acclimatization is more thoroughly documented for heat

exposure, be it metabolically or environmentally induced (Folk 1974, 1981, Fox 1974,



Bruce 1960, Berglund and McNall 1973, Givoni and Goldman 1973). The primary

physiological response to prolonged heat stress induced by a regime of work in heat is

an increased sweating capacity for a given heat load. Other changes related to

thermoregulatory sweating include a fall in the setpoint body temperature at which

sweating begins, triggering the onset of sweating earlier. A heat acclimatized person

also achieves a better distribution of sweat over their skin compared to an

unacclimatized person under the same heat load. Faced with comparable levels of heat

challenge, the heat acclimatized person also demonstrates a variety of cardiovascular

responses such as reduced heart rate, an increased blood volume and peripheral blood

flow (Fox 1974, Bean and Eichna 1943, Hardy 1961, Wyndham 1970). Acclimatization

to heat takes place mainly in the first week of exposure, while a longer period is required

for cold acclimatization or for resting or sedentary activity (Bruce 1960).

This picture of acclimatization can be regarded as most appropriate to hot-dry climate

zones. The pattern in hot-humid climates, however, differs significantly (Gonzalez et al

1974, Goldman et al 1965). In particular, the elevated capacity for sweating observed in

hot-dry situations seems to be less important in the humid condition due to the reduced

evaporative potential of the environment. Thus, while sweat secretion in the humid

acclimatized subject is initiated at a core temperature lower than that for the

unacclimatized subject, the shortfall in body heat dissipation in the humid condition

appears to be taken up by increased dry heat losses from the skin which result from an

increased peripheral blood flow and skin temperature.

Acclimatization is an unconscious feedback loop mediated by the autonomic nervous

system, that directly affects our physiological thermoregulation setpoints. Like

behavioral adjustment depicted earlier, the physiological feedback process of

acclimatization can also be depicted schematically:

outdoor indoor physiol. strain discomfort & climate climate & regulation dissatisfaction Acclimatization

Figure 1.4: Physiological feedback loop



1.3.3 Psychological feedback -- habituation and expectation

Psychological adaptation encompasses the effects of cognitive and cultural variables,

and describes the extent to which habituation and expectation alter thermal perceptions.

This concept has been most clearly elaborated under the banner “adaptation-level

theory” (A-LT). A-LT introduces the notion of optimal levels of stimulation, or adaptation

levels, along with a view of environmental stress resulting from excessive deviations

from such optimal levels. These optimal adaptation levels result from past exposure,

and act as benchmarks for environmental evaluations (Wohlwill 1974, Helson 1964).

Studies of the general nature of perception and its relationship to environmental stimuli,

memory and cognition, and contextual factors such as building type or season, can also

offer insights into understanding thermal comfort in buildings (de Dear et al 1991c,

Helson 1971, Ittelson 1973, Auliciems 1981, Russell and Ward 1982).

The role of expectation in thermal comfort research was acknowledged in the earlier

work of McIntyre (1980), who stated that “a person’s reaction to a temperature which is

less than perfect will depend very much on his expectations, personality, and what else

he is doing at the time.” Although the least studied of the three adaptive mechanisms,

psychological adaptation might actually play the most significant role in explaining the

differences between observed and predicted thermal responses. This applies

particularly in light of different environmental contexts such as the laboratory vs. home vs.

office, or when comparing responses in air-conditioned vs. naturally-ventilated buildings

(Fishman and Pimbert 1982, Heijs and Stringer 1988, Bush 1990, de Dear et al 1991c,

Rowe et al 1995, Oseland 1995,).

In terms of a feedback loop that can be incorporated into our conceptual model of

adaptation, expectation and habituation are influences by one’s current thermal

experience or one’s longer history of experiences with both the indoor and outdoor

climate. This in turn directly affects our thermal sensation and cognitive assessments of

thermal acceptability as described in Figure 1.5.



outdoor indoor physiol. strain thermal discomfort climate climate (Tsk, wet) sensation dissatisfaction Climatocultural practices & norms, Expectation HVAC & architecture & Habituation

Figure 1.5: Psychological feedback loop

1.4. Literature review

The relevant literature for this project is classified into two broad categories: 1)

climate chamber evidence for adaptation to climate, and 2) field evidence for

adaptation. Within the second category, we review some of the earliest studies of

adaptation, as well as an analysis of more recent, rigorously conducted field studies in

both air-conditioned and naturally ventilated buildings. The literature review of field

studies will be further sub-classified in terms of specific evidence for both behavioral

and psychological adaptation

1.4.1. Climate chamber evidence for adaptation to climate

A research design for experiments known as the “preferred temperature method” has

been applied by various researchers over the years to the questions raised by the

adaptive hypothesis. This method is very suitable for testing the adaptive feedback in a

laboratory setting because the environmental temperature within the chamber is directly

controlled by its single occupant, the subject. What follows is a summary of some of the

more pertinent results.

Fanger et al (1977) investigated the effects of differing climatic experiences, and by

implication, adaptive states, on thermal comfort responses by comparing the

temperature preferences of climatically disparate samples. In one study, sixteen Danish

subjects wore a standard 0.6 clo ensemble and sat quietly in a string chair (assumed to

exert negligible effect on their clothing insulation), one-at-a-time in a climate chamber for

2.5 hr. Subjects were selected for the study because of their regular swimming in the

ocean off Copenhagen during winter (lat. 56°N, mean February air temperature 0°C).

The sample was found to have the same preferred temperature, about 25.5°C, as



regular Danish college students (not winter swimmers) under the same experimental

conditions (Fanger and Langkilde 1975). Another Danish sample with cold exposures

consisted of 16 meat-packers from a refrigerated storeroom (Fanger et al 1977). They

too had the same preferred temperatures as the winter swimmers and college students.

If cold exposure fails to influence temperature preference, the next question is whether or

not heat exposure has an effect. As noted earlier, physiologists have a clearer picture of

heat, as opposed to cold, acclimatization, and much of that work refers specifically to

heat stress conditions of the type induced by a regime of work in heat. Very little

research has been done into the effects of acclimatization on thermal discomfort in the

moderate heat stress range. In one such study, Fanger (1972a) recruited a sample of

16 long-term inhabitants of the tropics shortly after their arrival in Copenhagen. The

same procedure as described above was followed, and the result, again, was that

temperature preferences were not significantly different.

Acknowledging the limited “shelf-life” of physiological heat acclimatization, de Dear et al

(1991b) replicated Fanger’s tropical experiment on location in Singapore (lat. 1°N)

using a sample of 32 college students. Attention to detail in the replication went as far

as borrowing the standard 0.6 clo KSU uniforms from Fanger's Danish laboratory, and a

chair similar to the Danish string chair was also used. Again, temperature preferences

turned out not to be significantly different from those of Fanger's benchmark Danish

subjects ~ circa 25.5°C (de Dear et al 1991b).

Gonzalez (1979) studied the role of natural heat acclimatization (humid) during a five day

heat wave in New Haven Connecticut during which day-time temperature maxima

ranged between 32°C to 37°C and 88% to 90% rh. Twenty young male subjects

participated. For lightly exercising subjects (116 W m-2), there was a discernible

increase in preferred temperature (as assessed by a rating scale) after the heat wave

(Gonzalez 1979). However, there were no statistically significant differences in thermal

comfort or acceptability responses of resting subjects between the before-and-after

heat wave tests.

The only significant departure from this picture of overall consistency in chamber

research results has been a recent, but as yet unpublished, PhD thesis from the



University of London (Abdulshukor, 1993). Three results from that study have been cited

by Humphreys (1994a):

• Chinese subjects in a Malaysian climate chamber preferred a temperature of

28.0°C,

• Malay subjects in a Malaysian climate chamber preferred an even warmer

temperature at 28.7°C, while

• Malay subjects in a London climate chamber study preferred only 25.7°C.

A clear implication of these results is that the hot and humid climatic context of the Malay

peninsular was responsible for a three degree elevation of temperature preferences.

These Malaysian climate chamber results are perplexing insofar as the same ethnic

groups (Chinese and Malays) with exactly the same thermal histories and experiences

(Singapore lies at the tip of the Malay peninsula) were represented in the de Dear et al.

(1991b) chamber study. Using exactly the same clothing, metabolic rate and

experimental protocol as used in Fanger’s Danish studies, the temperature preferences

in Singapore’s climate chamber were three degrees cooler than these unpublished

Malaysian results.

In conclusion, on the basis of the majority of experimental evidence published to date,

subjective discomfort and thermal acceptability under conditions most typically

encountered in residences and office buildings, by resting or lightly active building

occupants, appear to be unaffected by the physiological processes of acclimatization.

1.4.2. Field evidence for adaptation

While chamber studies have the advantage of testing under carefully controlled

conditions, field studies are best used for assessing the potential impacts of behavioral

or psychological adaptations as they occur in realistic settings. If people feel thermally

comfortable in conditions that fall outside of the ASHRAE comfort zone, it seems likely

that adaptation has played a role. While the majority of published field studies collected

the necessary data to determine whether people are comfortable when conditions are in

or out of the comfort zone, only a subset of the data contains sufficient detail to

disentangle the causal mechanisms behind those responses. In other words, exactly

what kind of adaptation was taking place?



1.4.2.1. The earlier field evidence for adaptation

Subjective assessments of thermal comfort typically use the rating scale method

(McIntyre 1978), where comfort is operationalized as a vote coinciding with the central

category of a thermal sensation, or comfort scale (“neutral”, or “comfortable”). The

ambient temperature found by statistical analysis to most frequently coincide with this

central rating is referred to as the sample's "neutrality" and is denoted here as Tn. The

typical cross-sectional field study consists of a questionnaire with rating scales

administered to building occupants while simultaneously recording indoor climatic

variables. The most important of which is air temperature. The simplest of these

studies are based on single-point measurements of temperature, and possible humidity.

Numerous such studies have been published over the years, and Humphreys' (1975)

review of 36 examples from various countries around the world uncovered a strong

statistical dependence of thermal neutralities (Tn) on the mean levels of air or globe

temperature (Ti) recorded within the buildings:

Tn = 2.56 + 0.83 Ti (r=+0.96) eq.1.1

It was noted that building occupants were able to find comfort, assumed to be a vote on

the central category of rating scales, in indoor temperatures spanning more than 13 K.

Humphreys (1975) attributed this to the adaptive processes, concluding that "... the

range of recent experience is better regarded as one of the factors which will contribute

to the acceptability of the environment to which the respondent is exposed."

Reasoning that indoor temperatures are dependent on outdoor temperatures to varying

extents, Auliciems suggested that there might be a statistical relationship between

indoor thermal neutralities and outdoor climate as well (Auliciems 1969).

Parameterizing “outdoor climate” as mean monthly temperature (i.e. average of the

average daily minima and average daily maximum for the month in question),

Humphreys (1978) followed up Auliciems’ suggestion and found convincing evidence for

adaptation to outdoor climate, as depicted in Figure 1.6. The influence of external

climate on indoor neutralities is particularly evident in the results from the so called "free

running" buildings which had neither centralized heating nor cooling plant (naturally



ventilated). In such buildings, the following linear regression model accounted for 94%

of the variance in neutralities:

Tn = 11.9 + 0.534 Tm (r=+0.97) eq.1.2

Climate controlled (centralized HVAC) buildings, on the other hand, had a less

pronounced but still highly significant correlation with outdoor mean monthly temperature

(Tm), but with a curve rather than a straight line achieving the best fit:

Tn = 23.9+.295(Tm-22) * exp(-((Tm-22)/(24*√2))2) (r=+0.72) eq.1.3

Auliciems (1981) subsequently revised Humphreys’ regression database by deleting

incompatible field studies, such as those based on asymmetric rating scales or children

as subjects, and adding more recent studies that had been published after Humphreys’

(1976) paper. These revisions brought the database up to 53 separate field studies

from various climatic zones in Australia, Asia, the Americas and Europe. After

collapsing free running and climate controlled buildings together, the resulting equation

was:

Tn = 0.48 Ti + 0.14 Tm + 9.22 (r=0.95) eq.1.4

where r is the multiple correlation coefficient. Even though the regression coefficients

may be unstable in such a model due to intercorrelation between the two independent

variables, equation 1.4 represents a widely cited statistical expression for the adaptive

hypothesis of human thermal perception.

While the statistical association between neutralities and prevailing outdoor climate

appears quite strong and convincing in Figure 1.6, the actual causal mechanism is left

in doubt by such “black box” adaptive models. Apart from thermal habituation and

acclimatization, there are several other plausible hypotheses, including the possibility

that some unmeasured variables in the human body's heat balance were compensating

for environmental temperature. For example, adjustments such as reduced clothing,

metabolic and humidity levels may combine with higher air velocities in the warm climate

studies (to the right-hand side of Figure 1.6) to cause subjects to experience thermal

neutrality at considerably higher indoor temperatures than would otherwise have been



the case. Therefore, to more rigorously test the physiological and psychological bases

of the adaptive hypothesis, these behavioral alternatives need to be eliminated, or at

least accounted for. More recent field studies and experiments have done just that, by

collecting simultaneous measurements of all of the input variables to Fanger’s PMV

model (ISO, 1994). Such studies allow a closer look at the causal mechanisms driving

thermal adaptation indoors.

FIGURE 1.6: The statistical dependence of indoor thermal neutralities on climate

(after Humphreys, 1976)

1.4.2.2. Analysis of neutral temperatures using recent field experiments

de Dear’s Ph.D. thesis, entitled “Perceptual and Adaptational Bases for the

Management of Indoor Climate - A Study of Warm Climates” (1985) and subsequent

ASHRAE Transactions paper (de Dear and Auliciems, 1985) reported on six thermal

comfort experiments in office buildings scattered across various Australian climatic

zones, ranging from equatorial (Darwin) through sub-tropical (Brisbane) to mid-latitude

14

16

18

20

22

24

26

28

30

32

-6 -1 4 9 14 19 24 29 34mean monthly outdoor temperature (C)

ind

oo

r n

eutr

ality

(C

)

climate controlled buildings

free running buildings



(Melbourne). The research design was premised on a consistent field method across

the various climatic and building types, including instrumentation, questionnaire,

protocols and analysis, thereby permitting climatic and contextual effects to be

disentangled from the dozens of methodological artefacts that potentially confound

earlier investigations. In both Melbourne and Brisbane, two experiments were

conducted during their respective summer seasons, one in free running buildings and

the other in climate controlled buildings. In total, these Australian samples included over

1100 office building occupants who cast questionnaire assessments of indoor climatic

environments on 3290 separate occasions. Figure 1.7 contains the neutralities,

estimated by probit analysis, for the Bedford scale in each of the six experiments,

plotted against the corresponding mean monthly outdoor temperatures. Neutralities

tend to increase from Melbourne's mild summer through to equatorial Darwin. This trend

is most pronounced in the free running (FR) buildings (codes F for Brisbane, C for

Melbourne). The Brisbane sample had the warmest neutrality in Australia at 25.6°C,

while Melbourne's FR sample had the coolest at 21.8°C. The climate controlled

buildings in Australia on the other hand all had neutralities clustered within the 23-24°C

range.

Apart from the neutralities observed in the six Australian field experiments, neutralities

predicted by Auliciems' thermal adaptive model (eq.1.4) are also shown in Figure 1.7,

as are the predictions based on the PMV heat-balance model. It should be noted that

these PMV predictions differ from those presented in the original publication (de Dear

and Auliciems, 1985). Average clo values observed in the experiments have since

been increased by 0.15 clo units to account for the insulation value of a typical office

chair (Schiller 1990, Fanger and Wyon 1990, McCullough and Olesen 1994). This

having the net effect of lowering the PMV model's neutrality predictions by over a full

degree, which in turn halves the average prediction error down to 0.7°C (absolute value).



18

20

22

24

26

28

30

-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

mean outdoor temperature (oC)

ind

oo

r n

eutr

ality

(oC

)

Observedneutrality

Adaptivemodel

Static PMVmodelP

A O

B

M

C

DE

F

G N

H

I

J

K LClimate-controlled

Free-running

Figure 1.7: Thermal comfort experiments in the field: Observed and predicted neutralities in relation to outdoor climate

Also depicted in Figure 1.7 are some results from six ASHRAE-sponsored Class I field

experiments in climate-controlled buildings across a variety of climatic contexts. Two

experiments are from San Francisco (Schiller et al 1988a; Schiller 1990). Another two

Code Location & season Climate-controlled or Free Running

Author

P Montreal-Winter CC Donnini et al (1996) A San Francisco-winter CC Schiller et al (1988a) O Montreal-Summer CC Donnini et al (1996) B San Francisco-summer CC Schiller et al (1988a) M Townsville-Dry CC de Dear + Fountain (1994) C Melbourne-summer FR de Dear + Auliciems (1985) D Melbourne-summer CC de Dear + Auliciems (1985) E Brisbane-summer CC de Dear + Auliciems (1985) F Brisbane-summer FR de Dear + Auliciems (1985) G Darwin - Dry CC de Dear + Auliciems (1985) N Townsville-Wet CC de Dear + Fountain (1994) H Singapore FR de Dear et al (1991) I Singapore CC de Dear et al (1991) J Bangkok FR Busch (1990) K Darwin-Wet CC de Dear + Auliciems (1985) L Bangkok CC Busch (1990)



are from tropical Townsville (de Dear and Fountain 1994), and another pair from

Montreal (Donnini et al, 1996).

Plotted along with the San Francisco observed neutralities are some predictions from

Auliciems' (1983) adaptive model as well as Fanger's PMV (heat balance) model, after

the effect of chair insulation (0.15 clo) has been added to Schiller et al's published

clothing insulation estimates. Clearly in both seasons, the adaptive model comes very

close to observation, but so too does the static heat balance model. This general

pattern of consistency between neutralities observed in air-conditioned buildings and

PMV predictions also extends to the more recent ASHRAE-sponsored studies in office

buildings located in a hot-humid climate (de Dear and Fountain 1994) and cold climate

(Donnini et al, 1996).

Busch's (1990) field experiments in office buildings in tropical Bangkok have also been

included in Figure 1.7. Both climate controlled (air-conditioned) and free running

buildings were studied, so a diverse range of thermal environments was covered by the

sample size of 1146. For the climate controlled buildings, neutrality was established at

24.5°C (code L in Figure 1.7), within a degree of the PMV prediction based on Busch's

mean clo value of 0.56 plus some chair insulation (0.15 clo). In Bangkok's free running

buildings, Busch observed a neutrality of 28.5°C (code J in Figure 1.7), which appears

to be over three degrees (K) warmer than predicted by Fanger's PMV. Auliciems'

(1983) adaptive model, on the other hand, came within half a degree of the observed

result. Busch suggested that the lighter clothing and higher local wind explain most of

the disparity between observed thermal neutralities in the naturally ventilated and air-

conditioned buildings, implying that behavioral adjustments were playing a strong

adaptive role. But there are clearly other factors at play, as well. Noting that clothing

and air velocity are used as input parameters to the heat balance models, the fact that

PMV still underestimates neutrality suggests that occupants were influenced by other

modes of adaptation unaccounted for by the heat balance inputs. In particular, PMV’s

underestimation of thermal neutrality more significantly in the free running building

sample than in the climate controlled building sample suggests that context and

adaptive opportunity can influence expectations and thermal response to the indoor

environment.



Another example of this is found in a more recent field experiment, in which de Dear et

al (1991c) examined climate controlled office buildings and free running residential

apartment blocks in equatorial Singapore. As seen in Figure 1.7 (code I), the observed

neutrality of 24.2°C in the air conditioned buildings was accurately predicted by both the

adaptive and heat balance models after 0.15 clo chair insulation was added to clothing

estimates. But as with Busch's Bangkok experiment, the 28.5°C neutrality observed in

Singapore's naturally ventilated apartment buildings (code H) was most closely

approximated by the adaptive model with a prediction of 27.2°C.

1.4.2.3. Evidence for behavioral adaptation - personal/environmental adjustment

There have been a few studies that examined direct evidence of exercised control, or

adjustment. One of the earlier studies that looked closely at clothing patterns was by

Fishman and Pimbert (1982), who studied 26 subjects in a UK office building for an

entire year. The estimated clo values of the Watson House sample had a strong linear

dependence on outdoor weather and season, especially in the case of women subjects,

with a regression gradient of -0.02 clo units per degree of outdoor mean weekly

temperature. This supports the hypothesis that the statistical dependence of indoor

neutrality on outdoor climate may, in part, be due to behavioral adjustments that directly

affect the heat balance, rather than acclimatization or habituation.

This hypothesis is also supported by the work of Humphreys (1994b) and Nicol et al

(1994), in which a study of naturally ventilated buildings in North West Pakistan

concluded that the office workers were comfortable across a wide range of seasonal

temperatures (neutralities varying between 15.7°C in winter, and 26.4°C in summer).

They also concluded that 1~B of the seasonal changes in comfort temperature could

be attributed to the flexibility in the traditional Pakistani clothing worn.

Personal behavioral adjustments over time were looked at in an exploratory study by

Nicol and Raja (1996) in the UK. They found that clothing changes were more strongly

dependent on the succession of outdoor temperatures that occurred prior to the

measurement, compared to the instantaneous or daily mean outdoor temperature, or the

instantaneous indoor temperature. This suggests the importance of time-series

measurements in future field studies designed to evaluate the effect of behavioral



adaptation on thermal comfort. Posture is another example of behavioral adaptation,

and they found a correlation with temperature such that posture would change to

increase the effective body surface area available for dry and latent heat exchange as it

got warmer.

In addition to adjusting to the environment, one can directly manipulate the environment

itself. Baker and Standeven (1994) used hourly questionnaires to ask whether subjects

had made adjustments to their clothing or to furniture, doors, windows, shades, fans or

any other part of the building to improve their comfort. Results indicated extensive

occupant-environment interaction - for 23 subjects in 7 buildings, over a total of 864

hours - there were a total of 273 adjustments to controls or other environmental aspects

of the room, and 62 adjustments to clothing.

The extent to which adjustments actually improve thermal comfort is as important as the

frequency with which they’re made. Benton and Brager (1994) conducted a field

experiment of thermal comfort in a centrally-conditioned office building in California,

before and after energy-efficiency retrofit measures were installed. Adaptive opportunity

was addressed by a series of questions on the availability, use, and effectiveness of

coping mechanisms that either altered the physical environment or personal variables.

While modification mechanisms were infrequently cited, when exercised, they

consistently received high ratings for effectiveness. Behavioral mechanisms received

the highest number of citations, and clothing adjustments in particular were given a

relatively high effectiveness rating.

1.4.2.4. Evidence for psychological adaptation - expectation and context

While there is limited field data providing direct evidence for the effects of psychological

adaptation on thermal comfort, the previous analysis of Figure 1.7 suggests that it can

be implied through comparing comfort responses in different contexts. Paciuk (1990)

provided a more direct analysis of the distinction between available control (adaptive

opportunity), exercised control (behavioral adjustment) and perceived control (related to

the psychological dimension and expectation). She found that, in addition to the

traditional list of thermal inputs to the heat balance models, perceived degree of control

was one of the strongest predictors of thermal comfort in office buildings, and had a



significant impact in shaping both thermal comfort and satisfaction. This finding was

also supported by the work of Williams (1995), in her study in office buildings in the

Northwest of England. The subjects in this study expressed higher levels of satisfaction

when they perceived themselves to have more control over their environment.

Increasing levels of both perceived and available control have implications for the

design of buildings, including their mechanical systems and interior layouts. A good

example is shown in Figure 1.8, which comes from the English researchers Leaman and

Bordass (1993). They administered a standardized indoor environmental quality

questionnaire to thousands of office workers across the UK, and found a strong negative

relationship between perceived control and occupant density in the workplace.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1 2-4 5-9 10-29 30+

Number of Occupants in Office

Per

ceiv

ed C

on

tro

l R

atin

g

ventilation

heating

Figure 1.8: Relationship between the number of workers sharing an office and perceived level of control over room heating and ventilation systems. (Leaman & Bordass 1993).

This relationship also has implications for air-conditioned vs. naturally-ventilated

buildings. Naturally ventilated buildings typically consist of small offices with single

occupants or small groups of who are usually within reach of an operable window. This

is clearly not the case in most modern air-conditioned office buildings which are



characterized by deep-space or open-plan floor layouts with dozens if not hundreds of

employees being required to share the same space. The effects of this may be evident

in Figure 1.7, where the naturally ventilated buildings had thermal neutralities

significantly different from the predictions of heat-balance (static) models such as PMV.

These same buildings probably had occupants who perceived a higher degree of

personal environmental control by comparison to their counterparts in centrally air-

conditioned office buildings. The poor predictive capabilities of PMV in naturally

ventilated buildings suggests that adaptive processes other than behavioral adjustment

(which would be accounted for in the heat balance models) must be occurring.

Expectation seems the most likely explanation, since expectation has all but been

eliminated by the climate-chamber method of comfort research. Within the adaptive

hypothesis, such buildings would be expected by their occupants to provide variable

indoor temperatures, and therefore be judged less critically than centrally air-conditioned

buildings. The RP-884 data analysis will pay careful attention to the distinction between

thermal perception in air-conditioned vs. naturally ventilated buildings.

Although naturally ventilated buildings might generally offer a higher level of adaptive

opportunity than air-conditioned buildings, they could still differ in the actual degree of

occupant control they offer. Rowe (1995a) looked at studies in 1) air conditioned

buildings, 2) naturally ventilated buildings, and 3) naturally ventilated buildings with

supplementary on-demand cooling and heating equipment. He found a significantly

higher level of satisfaction in the naturally ventilated buildings with additional

supplementary control. This led to the conclusion that people have a wider tolerance of

variations in indoor thermal conditions if they can exert some control over them, and that

a considerably higher level of satisfaction will be reached if occupants have means of

controlling the upper and lower temperature limits. In Fishman and Pimberts’ (1982)

year-long study in a UK office building, seven of the 26 subjects worked in air-

conditioned areas. The rest were in naturally ventilated offices. While the sample size

was not large, there was still a difference in the thermal responses of these two groups

as temperatures rose above 24°C. People in the air-conditioned offices began voting

much higher on the thermal sensation scale than their colleagues in the naturally



ventilated work areas, suggesting that they were less tolerant of higher temperatures

and expected homogeneity in their thermal environment.

Several other researchers support this hypothesis regarding occupant expectations and

their effects on thermal perception. In a study conducted by Black and Milroy (1966) in

both air-conditioned and non-air-conditioned office buildings in London, occupants in

the air-conditioned buildings expressed more complaints about temperature

fluctuations, even though the free running building experienced much greater variability.

The occupants were basing their evaluations on the benchmark of their own

preconceptions of what air-conditioning should achieve, rather than on what it actually

provided. In effect, this suggests that increasing levels of sophistication in

environmental control systems and building services are on a treadmill of attempting to

satisfy ever-increasing occupant expectations (de Dear and Auliciems 1986). Another

study by Rohles et al (1977) found that Michigan subjects were more tolerant of high

indoor summer temperatures (32°C ET*) than Texan subjects. Since other heat balance

variables such as clothing or activity could not account for the difference, it was

speculated that the Texans took summer air-conditioning for granted and came to

expect or even demand cool temperatures, therefore becoming more critical of warmer

indoor conditions than their northern counterparts.

1.5. Implications for RP-884

1.5.1. Lessons from static heat balance models

We believe that the split between “adaptive” and “static” heat balance models, or

schools of thought, is not as irreconcilable as the protagonists have suggested. As

mentioned previously, the terms "static” and “constancy" have given rise to a mistaken

idea that models such as PMV and 2-node, plus the thermal comfort standards based

on them, prescribe a single, constant temperature for thermal comfort the world over.

But the PMV and 2-node models do, in fact, predict comfort temperatures moving in the

direction of prevailing outdoor climate -- as seen in the offset of winter and summer

comfort zones in the last few revisions to ASHRAE’s Standard 55. So the static model

of comfort is in reality an “adaptive” model in its own right -- the fundamental distinction

between the static and adaptive models is their underlying basis or postulated cause for



the shift in comfort temperatures. The former permits only behavioral adjustments

(personal/technological) to heat balance variables such as clothing or air velocity,

whereas the original adaptive models were premised on changing physiological (i.e.

acclimatization) and psychological (i.e. expectations/habituation) setpoints. While this

may seem to be a fine distinction, failure to appreciate it has, in the opinion of the

authors, been responsible for unnecessary controversy between the two sides of this

debate. An important contribution of the RP-884 adaptive model will be to go beyond

the “black-box” approaches of the earlier adaptive models, so that we can better

understanding the underlying processes of adaptive comfort.

Understanding the challenges of applying laboratory-based static models in the field can

provide guidance on issues to consider when developing a new adaptive model that

combines the best of both static and adaptive theories of thermal comfort. One place to

start is to learn from some of the explanations that have been offered for the

discrepancies between predicted and observed thermal sensations in real buildings:

1. Estimating insulation of clothing garments or ensembles. Brager et al. (1994) have

demonstrated the significance of the clothing insulation estimation method on the

actual clo value obtained. The ensemble insulation value differs by as much as 20%

depending on whether one uses the tables and algorithms in the older or newer

versions of ASHRAE Standard 55 (1981, 1992), or ISO 7730 (1994). It will therefore

be important that rigorous statistical correction factors are used to create consistent

ensemble clo values across the RP-884 database.

2. Accounting for the chair insulation. The tendency for PMV to overestimate thermal

neutralities has been reported in several field studies (Schiller 1990), prompting

Fanger and Wyon (1990) to suggest that the method of estimating clothing insulation

might be systematically flawed by omission of the thermal effect that chairs have on

their occupants. McCullough and Olesen (1994) responded by examining the effects

of upholstered office furniture on the total thermal insulation of a heated manikin, and

found that a typical office chair adds approximately 0.15 clo to the value that one gets

by simple addition of individual garment values, as described in ASHRAE Standard

55-92 (ASHRAE 1992) or ISO-7730 (ISO 1994). Even if the original researchers



supplying raw data to the RP-884 database omitted the effect of chair insulation, it

will be included as part of the RP-884 analysis.

3. Non-uniformities of physical measurements. If field studies take spot-

measurements of general ambient thermal parameters that are separated from the

occupant’s location in space and/or time, then they might not be representative of

what the occupant is actually experiencing at all (Baker 1993). This becomes

particularly important in rooms with transient or spatially non-uniform thermal

conditions, which are more likely to be the case in passive, or naturally ventilated

buildings, or any situations where workers have high levels of personal or

environmental control available to them. An analysis of adaptive comfort would best

be served by using data taken close to the occupant’s location, and at the same time

as the thermal questionnaire. This will be carefully considered when selecting data

for inclusion in the RP-884 database.

4. Behavioral adjustments and perceived control. People adapt to the environment by

adjusting their clothing or activity, modifying their posture or moving to another part of

the room, opening/closing windows, operating fans or other environmental controls.

But why would this cause a discrepancy between the observed and predicted

conditions? In theory, static heat balance models account for clothing, activity, and

thermal environmental parameters, and should therefore, be able to factor the

consequences of the behavioral adjustments into their equations. Probably the most

likely impact of thermal adjustments is the perception of control -- psychologists are

quick to point out that adverse or noxious stimuli are less irritating if the subject

perceives she/he has control over them (Paciuk 1990, Veitch and Arkkelin 1995,

Kaplan and Kaplan 1982). Issues of behavioral adjustment and perceived control will

be given a high priority in the RP-884 analysis, as this represents a potentially

significant feedback loop between discomfort and purposive behavioral

thermoregulation.

5. Thermal sensation, preference, and acceptability. Existing thermal comfort

standards provide guidelines for “thermal acceptability”, while the static heat balance

models on which they’re based only predict “thermal sensation”. As a result, the



traditional approach has been to indirectly associate specific thermal sensations with

“acceptability”, and to assume that thermal “preference” is synonymous with thermal

“neutrality”. RP-884 will strive to include field experiments in its database that directly

asked about sensation, acceptability and preference, so these assumptions can be

tested.

1.5.2. Time scales of thermal adaptation

Since each class of adaptive response depends on repeated exposure to a given

regime of thermal conditions, the questions of duration of exposure and lag in response

seem relevant to adjustment, acclimatization and habituation adaptive processes. A

review of the literature in this area will reveal, in part, which mechanisms are likely to

play the most significant role in thermal response to the indoor environment and,

therefore, which should receive the greatest attention in the RP-884 analysis.

The significance of the temporal dimension of thermal adaptation is realized when one

considers applications of adaptive models to control algorithms for HVAC systems.

Auliciems was the first to propose such an adaptive algorithm (Auliciems 1986) which

he referred to as a thermobile (as opposed to a thermostat). It was premised on the

adaptive model described in equation 1.4. The question of how long the averaging

period for the algorithm’s temperature inputs should be was left open but, as an initial

guess, Auliciems proposed that the running means, one for both indoor and outdoor

temperatures, should comprise hourly observations across the preceding fortnight.

More recently, Humphreys and Nicol (1995) proposed a similar adaptive algorithm for

UK office temperatures. The gist of his proposed guideline is that a weighted, running

mean of the preceding week’s outdoor temperature is combined with current outdoor

temperature in a ratio of 3:7, thereby reflecting the overriding importance of today’s

weather on clothing decisions and behavior. Humphreys proposed that this outdoor

temperature index be used to specify the target indoor temperature.

Adjustment. Thermal adjustment and behavioral adaptation operate across several

time scales. Cutaneous thermoreceptors provide almost instantaneous neural

information about sudden changes in the thermal environment. For example, as

experienced, when crossing the indoor/outdoor threshold, thus enabling clothing



adjustments and other behavioral adaptations to be effected well in advance of any

significant alteration in the body’s heat balance. As for other behavioral adaptations,

very little research has been published on adaptive time lags. A notable exception is a

study by Humphreys (1979) on clothing adjustments at the seasonal and synoptic

weather time-scales. He was able to statistically relate clothing insulation levels on any

given day to an exponentially weighted moving average of outdoor temperatures on the

days leading up to, and including, the day in question. It was suggested that the half-life

for daytime clothing regulation was of the order of 20 hours.

Acclimatization. The literature on acclimatization reviewed earlier indicates that the

physiological adaptations to heat exposure begin on the first day of exposure and

progress rapidly to full development by the third or fourth day, providing the heat

exposures are sufficiently severe to elevate core temperatures (Bean and Eichna, 1943;

Fox, 1974). This has been achieved experimentally with daily work-in-heat regimes or

hyperthermic suits. Passive exposures to heat in the course of normal day-to-day

acclimatization cannot be expected to induce acclimatization responses as quickly nor

as thoroughly, although Wyndham (1970) reports that passive exposures to the normal

course of the seasons in South Africa induced definite signs of at least partial

acclimatization. The time-scales of interest for office workers, therefore, may be of the

order of weeks to months.

Habituation and expectations. Unfortunately this literature review was unable to find

reference to any research on the time-scales of psychological adaptive responses,

probably for the simple reason that no researchers have previously attempted to

disentangle psychological from other thermal adaptive processes. However, anecdotal

evidence suggest that building occupants become accustomed to levels of warmth

prevailing within buildings on time scales of weeks to months. These scales translate

into synoptic and seasonal processes operating in the outdoor atmospheric

environment.

To summarize, the adaptive processes are operating on time scales ranging from

seasonal, through synoptic to diurnal. Critics of the adaptive approach at various

symposia or seminars have repeatedly asked the question: “... how long must your



people suffer in sub-optimal indoor climates before they become adapted?” Ignoring

the emotive language in this question, we feel its answer, if there is one, depends on

which of the adaptive processes is being relied upon. The consensus within what little

has been written on the temporal dimension of adaptation is that meteorological

conditions on the day in question, and to a lesser extent, the preceding week or two,

exert an overriding influence on thermal adaptation in general, and clothing

thermoregulation in particular. This has important implications for future field

experimental protocols. While traditional research designs tend to look at responses at

a given moment, experiments that intend to evaluate adaptive mechanisms need to take

measurements over extended periods of time. Available evidence reviewed in this

paper indicates that, in climate chamber experiments at least, the slower physiological

adaptive process of acclimatization appears not to be relevant to this question of

thermal neutrality and its fluctuations from day-to-day, week-to-week and season-to-

season. As a result, the RP-884 data analysis and model development will focus more

heavily on the adaptive mechanisms of adjustment, and habituation/expectation. This

also suggests the need for field experiments in which data were rigorously obtained,

including accurate measurements of air movement

1.6. Aims

The specific objectives of RP-884 can now be listed:

1. Elaborate and define adaptive processes in the context of indoor climatic

perception.

2. Develop an internally consistent and quality controlled database of thermal comfort

field experimental data from a variety of buildings and climates across the world. To

then make this database as widely available to other thermal comfort researchers

as possible.

3. Examine the semantics of thermal sensation, acceptability and preference scales

within the context of an adaptive model of thermal comfort.

4. Develop statistical models of thermal comfort based explicitly on the various

processes of adaptation, including adjustment, acclimatization and habituation.



5. Explore the influence of contextual and non-thermal factors on thermal perception

indoors. This investigation will include (but not be restricted to) season, building

purpose (residential, office) and climatic setting, on thermal perception. This will

inevitably include comparisons with the thermal comfort predictions of heat-balance

models such as PMV/PPD.

6. Proposing a variable temperature standard that, in time, might eventually

supplement and/or modify ASHRAE Standard 55.


Methods page MRL Australia 33

CHAPTER 2 - METHODS

2.1. Overview of the RP-884 approach

In view of the vital role played by perceptual and cognitive factors in the adaptive

hypothesis, a consensus emerging from the literature is that observational data to test

the hypothesis must be come from field rather than climate-chamber research. The

reductionist, laboratory approach to comfort research runs the risk of stripping away

those very aspects of thermal perception that are the focus of the adaptive hypothesis

(McIntyre, 1982). The approach in RP-884 has, therefore, been to focus on research

conducted in “real” buildings, occupied by “real” subjects going about their normal day-

to-day activities rather than paid college-age subjects sitting in the highly contrived and

controlled setting of the climate chamber.

In order to identify and disentangle various adaptive processes from the data, it became

apparent in the research design stages of RP-884 that the field data needed to be of a

high standard. The database underpinning RP-884’s adaptive models comprised field

experiments where the standard of measurements, both physical and subjective, was as

close as possible to laboratory-grade, and comprehensive enough to enable heat-

balance indices (static model) to be calculated. Where possible, the RP-884 database

comprised field experiments rather than field studies. Furthermore, the database

needed to be built up from the raw data files generated by the original researchers

instead of their processed or published findings. This approach allowed a variety of

quality controls to be applied and enhanced the internal consistency of the entire

database.

Considerable effort and resources from RP-884 and numerous field researchers around

the world have been dedicated to the assembly of this database of thermal comfort field

experiments. It therefore seems highly likely that the database will have numerous

applications well beyond the scope and lifetime of RP-884. Therefore a decision was

made to provide global and unrestricted access via the World Wide Web (WWW).



The ultimate application of any thermal comfort model, adaptive or otherwise, is to

predict the response of a given group of human subjects to a given set of input

parameters (temperatures, humidity, air speeds etc). Typically this means either the

occupants of an extant building, or the hypothetical occupants of a yet-to-be-built

structure. Since RP-884 adaptive models are to be applied at the level of single

buildings, the meta-analysis used to derive the models should be conducted at the same

unit of analysis -- that of the single building. Therefore the 21,000 rows of raw data in the

RP-884 database were subsequently sorted, aggregated and analyzed at the building

level. Figure 2.1 is a schematic depiction of the database process, and how it evolved

into the adaptive model meta-analysis. The remainder of this chapter describes the

detailed steps underpinning this schematic flow chart.



Figure 2.1: Schematic depiction of the RP-884 database process and its evolution into the adaptive model meta-analysis.



2.2. Establishing the database for RP-884

The RP-884 database is the project’s fundamental research resource. This section

describes where the raw data came from, how they were quality controlled, and what

processes of data assimilation were developed to ensure internal consistency within the

database.

2.2.1. Sourcing the raw data

The literature review in Chapter 1 uncovered numerous thermal field studies and

experiments. Combined with the authors’ and ASHRAE TC 2.1’s knowledge of

researchers currently or recently active in this area, we compiled a mailing list. An initial

fax was broadcast to dozens of researchers around the world requesting information

about field methods and soliciting contributions to the database (see Figure 2.3a and

Figure 2.3b). On the basis of the returns to that questionnaire, a list of the contributors

and their field methods was collated. Figure 2.2 depicts the geographic locations of the

contributors to the RP-884 database. Data came from four continents and a broad

spectrum of climatic zones.

Figure 2.2: Geographic origins of the raw data contributions to RP-884 world database of thermal comfort field research



Table 2.1: Sources of raw data for the RP-884 world database of thermal comfort Researcher File

No. Experiment Location Building

Type Research Design

Sample Size

No. of Blgds

Jill Brown (U of Wales - UK) 1 South Wales, UK (summer) HVAC cross-sectional 80 4

Jill Brown (U of Wales - UK) 2 South Wales, UK (winter) HVAC cross-sectional 38 4

John Busch (LBL) 3 Bangkok, Thailand (Hot season) HVAC cross-sectional 776 2

John Busch (LBL) 4 Bangkok, Thailand (Hot season) NV cross-sectional 392 3

Benton + Brager (ACT2) 5 Antioch, California (winter) HVAC longitudinal 111 1

Tri Karyono (Sheffield, UK) 6 Jakarta, Indonesia (summer) HVAC cross-sectional 458 5

Tri Karyono (Sheffield, UK) 7 Jakarta, Indonesia (summer) NV cross-sectional 97 1

Tri Karyono (Sheffield, UK) 8 Jakarta, Indonesia (summer) Mixed cross-sectional 41 1

Donnini ASHRAE RP-821 9 Montreal, Canada (summer) HVAC cross-sectional 443 12

Donnini ASHRAE RP-821 10 Montreal, Canada (winter) HVAC cross-sectional 426 11

de Dear (PhD data) 11 Brisbane, Australia (summer) HVAC cross-sectional 564 5

de Dear (PhD data) 12 Brisbane, Australia (summer) NV cross-sectional 611 5

de Dear (PhD data) 13 Darwin, Australia (dry season) HVAC cross-sectional 493 8

de Dear (PhD data) 14 Darwin, Australia (Wet season) HVAC cross-sectional 555 7

de Dear (PhD data) 15 Melbourne, Australia (summer) HVAC cross-sectional 512 4

de Dear (PhD data) 16 Melbourne, Australia (summer) NV cross-sectional 555 3

Guy Newsham (Canada NRC) 17 Ottawa, Canada (winter) HVAC longitudinal 1859 4

Nicol, Fergus (Oxford-Brooks U) 18 Karachi, Pakistan (summer) NV longitudinal 190 1

Nicol, Fergus (Oxford-Brooks U) 19 Karachi, Pakistan (winter) NV longitudinal 470 1

Nicol, Fergus (Oxford-Brooks U) 20 Multan, Pakistan (summer) NV longitudinal 437 1

Nicol, Fergus (Oxford-Brooks U) 21 Peshawar, Pakistan (summer) NV longitudinal 556 1

Nicol, Fergus (Oxford-Brooks U) 22 Peshawar, Pakistan (winter) NV longitudinal 513 1

Nicol, Fergus (Oxford-Brooks U) 23 Quetta, Pakistan (summer) NV longitudinal 492 1

Nicol, Fergus (Oxford-Brooks U) 24 Quetta, Pakistan (winter) NV longitudinal 425 1

Nicol, Fergus (Oxford-Brooks U) 25 Saidu, Pakistan (summer) NV longitudinal 568 1

Nicol, Fergus (Oxford-Brooks U) 26 Saidu, Pakistan (winter) NV longitudinal 548 1

Nick Baker, Cambridge UK 27 Athens, Greece (summer) NV longitudinal 1626 6

Raja, Ifitkhar (Oxford-Brooks U) 28 Oxford, UK (summer) NV longitudinal 877 3

David Rowe (U Sydney) 29 Sydney, Australia (summer) mixed longitudinal 137 1

David Rowe (U Sydney) 30 Sydney, Australia (winter) mixed longitudinal 170 1

Dav id Rowe (U Sydney) 31 Sydney, Australia (winter) HVAC cross-sectional 83 1

Gail Brager ASHRAE RP462 32 Bay Area, California (summer) HVAC mixed 673 7

Gail Brager ASHRAE RP462 33 Bay Area, California (summer) NV mixed 360 3

Gail Brager ASHRAE RP462 34 Bay Area, California (winter) HVAC mixed 923 7

Gail Brager ASHRAE RP462 35 Bay Area, California (winter) NV mixed 393 3

de Dear & Fountain 702-RP 36 Townsville, Australia (Dry season) HVAC cross-sectional 628 12

de Dear & Fountain 702-RP 37 Townsville, Australia (Wet season) HVAC cross-sectional 606 11

Ruth Williams (BSRIA - UK) 38 Merseyside, UK (summer) NV cross-Sectional 167 3

Ruth Williams (BSRIA - UK) 39 Merseyside, UK (winter) NV cross-Sectional 209 5

Ruth Williams (BSRIA - UK) 40 Merseyside, UK (winter) Mixed cross-Sectional 121 1

de Dear, Foo and Leow 41 Singapore (summer) HVAC cross-sectional 333 1

de Dear, Foo and Leow 42 Singapore (summer) NV cross-sectional 583 1

Bauman et al (Steelcase) 43 Grand Rapids, Michigan (winter) HVAC mixed 85 1

Benton + Brager (ACT2) 44 San Ramon, CA (summer) HVAC longitudinal 96 1

Benton + Brager (ACT2) 45 San Ramon, CA (winter) HVAC longitudinal 285 2

Benton + Brager (ACT2) 46 Auburn, CA (winter) HVAC longitudinal 128 1

TOTAL 20693

TOTAL 160



Figure 2.3a: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world



Figure 2.3b: The RP-884 thermal comfort research methods questionnaire sent to active field researchers around the world



2.2.2. Ratings of raw data submitted to RP-884

Field data were classified according to the standard of instrumentation and procedures

used for indoor climatic measurements. Three broad classes of thermal comfort field

investigation were defined as follows:

• Class III: Field studies based on simple measurements of indoor temperature and

possibly humidity. One level of measurement above the floor. Possibly asynchronous

and non-contiguous physical (temperature etc.) and subjective (questionnaire)

measurements. The field studies used to derive the previously published adaptive

models (Humphreys, 1976, 1978, 1981; Auliciems, 1981) were all Class III.

• Class II: Field experiments in which all indoor physical environmental variables (ta,

tr, v, rh, Icl, met) necessary for the calculation of SET* and PMV/PPD indices were

collected at the same time and place as the thermal questionnaires were

administered. Measurements may not have been made at the three heights above

floor level as specified in ASHRAE (1992) and ISO (1994) standards (0.1, 0.6 and

1.2m). Humidity measurements were taken by aspirated psychrometer or solid state

hygrometer sensors. Air speeds were measured by hot wire (or sphere) probes with

thresholds above 0.1 ms-1, directional sensing elements and time constants larger

than that necessary for turbulence intensity, Tu, assessments.

• Class I: Field experiments in which all sensors and procedures were in 100%

compliance with the specifications set out in ASHRAE Standard 55 (1992) and ISO

7730 (1984). In particular, all of the shortcomings identified in Class II investigations

were absent from Class I field experiments. Three heights of measurement with

laboratory-grade instrumentation including omnidirectional anemometry capable of

turbulence intensity assessments. The three ASHRAE-sponsored field experiments

in the San Francisco Bay Area (RP-462), Townsville (RP-702) and Montreal (RP-

921) are examples of Class 1 investigations.

Also listed in Appendix C is a comprehensive summary of each field project adopted in

the ASHRAE RP-884 database. Information listed includes: original researchers’

names, class of data (I, II or III); publications; field location, climate and season;

description of sample buildings; indoor climatic instruments; questionnaire details, and



outdoor meteorological/climatological data sources. In addition there is a detailed

section explaining the RP-884 standardization steps and procedures that were applied

to each project’s raw data before they were assimilated into the cumulative database.

2.3. Raw data standardisation

Individual researchers each have their own detailed methods, but thankfully these

idiosyncrasies are largely transparent to the readers of their final research publications.

However, in an exercise involving the assembly of a database from raw data, the

emphasis must be on standardization. In the present case this has not been easy since

the decision to assemble a database occurred after the original data were collected

(except in the case of the ASHRAE field RPs). This section describes some of the

more important steps in this process of data assimilation.

2.3.1. Creation of a standard data template

A standard template of variables was developed, based on previous ASHRAE-funded

research projects, particularly RP-702 (hot-humid), RP-462 (Mediterranean) and RP-

821 (cold climate). This template was applied to each and every row of data in the RP-

8884 database (n~21,000). The standard template consisting of units of measurement,

codenames and coding conventions is presented in Appendix E. The template is

broken down into the following groups of variables:

• Basic Identifiers such as building code, subject information and date.

• Thermal Questionnaire comprising sensation, acceptability and preference scales,

as well as activity, metabolic rates, clothing and chair insulation.

• Indoor Climate Physical Observations of air temperature, globe temperature, air

velocity and turbulence at three heights, plus dewpoint, rh and plane radiant

asymmetry temperature.

• Calculated Indices, including averaged single height measurements of air

temperature, mean radiant temperature, air velocity; operative temperature,

turbulence intensity, vapour pressure and relative humidity; new effective temperature,

new standard effective temperature, TSENS, DISC, predicted mean

vote, predicted percentage dissatisfied and draft risk at three heights and maximum.



• Personal Environmental Control covering questions of perceived control (including a

composite index that could be applied to buildings where the questionnaire did not

cover perceived control), and specific adaptive opportunities. Options include:

windows, internal doors, external doors, thermostat, curtains/blinds, local heaters and

fans.

• Outdoor Meteorological Observations include raw data and derived indices. Daily

temperatures and relative humidities at 600 hours and 1500 hours were collected,

and daily effective temperatures (ET*) for these times calculated with WinComf©

(Fountain and Huizenga, 1996). Daily averages for air temperature, relative humidity

and effective temperature were also calculated.

2.3.2. Consistent mean radiant temperatures within the database.

Mean radiant temperature was recalculated from each row of data using the ASHRAE HoF

formula (1993), based on raw globe and air temperatures plus air speed.

t (t 273)1.10 10 V

D(t t ) 273

r g4

8 0.6

0.4 g a

14

= + −

−+

•

•ε

where ε is emissivity (0.95 for a black globe), D is globe diameter (0.04 m for “ping-pong”), V is air speed in m s-1, ta is air temperature in oC, tg is globe thermometer’s temperature in oC. N.B. the globe thermometer has a lagged response and requires about 10 to 15 minutes to equilibrate. Larger diameter globes have longer lags.

2.3.3. Consistent comfort index calculations within the database

With models as complex as PMV and SET*, it is to be expected that several different

algorithms and implementations exist in engineering and research circles around the

world. ASHRAE TC 2.1 has recently acknowledged this potential source of “noise” in

comfort research and engineering applications, and has sought to standardize the

models into a single software package (ASHRAE RP-781) now known as WinComf©



(developed by Fountain and Huizenga, 1996). From this software, new effective

temperature (ET), new standard effective temperature (SET), the two-node temperature

sensation index (TSENS), the two-node discomfort index (DISC), Fanger’s Predicted

Mean Vote (PMV) and Fanger’s Predicted Percentage Dissatisfied (PPD) were

obtained for each row of data in each field experiment within the database, provided the

necessary input data were available. Requisite input data include: air temperature

(average of three heights, TAAV), mean radiant temperature (average of three heights,

TRAV), air speed (average of three heights, VELAV), relative humidity (RH), metabolic

rate (MET) and insulation afforded by clothing and chair (INSUL). Where only one

measurement height was available in the raw data files, it was treated as the average. If

TRAV values were missing TAAV was often substituted in its place.

2.3.4. Predicted draft risk index (PD)

Predicted Draft Risk was not taken from the WinComf© software. The following formula

was used:

PD = (34 - ta) * (v - 0.05)0.62 * (0.37 * v * Tu + 3.14)

ta = indoor air temperature (oC)

v = indoor air velocity (m s-1)

Tu = turbulence intensity (%)

ta, v and Tu were provided at three heights (0.1m, 0.6m and 1.1m above the floor).

Where possible PD was calculated at the three heights and designated as PD_L,

PD_M and PD_H. The maximum of these was then taken as the value of the index

(PD_MAX) for subsequent analyses. Where ta, v and Tu were only recorded at one

height the resulting PD defaulted to PD_MAX. Where no turbulence was provided in the

raw data files, the default of 40% was substituted in calculations and if the velocity was <

0.05 m s-1 the default value for PD was zero.



2.3.5. Clothing insulation in the ASHRAE RP-884 database

Clothing insulation has remained one of the more troublesome parameters for field

researchers. It is often singled out as an explanation whenever observed comfort responses

of building occupants depart from the predictions of thermal comfort standards and indices.

The insulation value of a particular ensemble of clothing can only be measured with any

precision using a thermal manikin in a controlled climate chamber over several hours, and

even then the answer varies considerably between manikins. Little wonder, therefore, that

the estimation of clothing insulation has presented HVAC practitioners with confusion when

applying comfort theory in the field, encouraging them to think of thermal comfort

simplistically in terms of single set-point temperatures instead of multidimensional comfort

zones.

But it is not only practitioners who have trouble with clothing insulation -- even researchers

face difficulties due to a complicated variety of garment insulation databases and equations

available to derive whole ensemble insulation values. Adding further confusion, even the

standards themselves (ISO 7730 and ASHRAE 55) have recommended different techniques

and equations between their various revisions. This leads to the surprising situation where a

given set of clothing may get quite different clo estimates attached to it, depending on which

standard and which edition is used.

From the RP-884 perspective, clothing represents one of the key thermal adaptive

responses. Clothing is a behavioral adjustment that directly affects the heat-balance.

Therefore this important parameter demanded careful treatment within the RP-884

database. The main approach here has been to establish statistical “conversion

factors” between the various clo estimation techniques based on a large sample of

clothing data from the ASHRAE RP-462 project in San Francisco (Schiller et al.,

1988a,b), and to use these to convert all clo data within the RP-884 database into

equivalent ASHRAE Standard 55-92 (ASHRAE, 1992) intrinsic ensemble clo

estimates.



2.3.5.1. Discrepancies between field estimation methods for clo.

Brager et al. (1994) indicated that conversions from ASHRAE Standard 55-81 to 55-92 clo

estimates lifted their sample average ensemble insulation by 0.1 clo, alerting the RP-884

team to the possible difficulties in comparing field data from several different researchers

around the world. Because its raw data file, as supplied to the RP-884 team, contained

individual clothing garment values (CL1 through CL15) instead of the usual aggregate

ensemble insulation estimates, the ASHRAE RP-462 project (Schiller et al 1988a,b)

afforded a unique opportunity to quantitatively compare clothing ensemble insulation

estimates based on several different techniques with each being benchmarked against the

ASHRAE Standard 55-1992 method:

• Sprague and Munson (McIntyre 1980),

• ASHRAE Standard 55-1981,

• ISO7730 1984,

• ISO7730 1994,

For each subject in the RP-462 field experiment spreadsheets, we estimated the

ensemble clothing insulation using each of the techniques listed above, and also the

ASHRAE 55-1992 technique. Following are some regression models fitted to the

relationships between the ASHRAE 55-92 estimates, and the other four techniques in

turn. The two genders were analyzed separately and all regression equations were

forced through the zero origin so that the fitted models could be applied throughout the

RP-884 database as simple conversion coefficients.



Male Clothing Estimate from RP-462 data

ASH55-92 = 1.0938 * ASH55-81R2 = 0.8072

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

ASHRAE 55-81 clo

AS

HR

AE

55-

92 c

lo

Female Clothing Estimate from RP-462 data

ASH55-92 = 1.2362 * ASH55-81

R2 = 0.6073

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

ASHRAE 55-81 clo

AS

HR

AE

55-

92 c

lo

Figure 2.4: Relationships between ASHRAE 55-81 and ASHRAE 55-92 clo estimates in the RP-462 field experiment

Figure 2.4 depicts the ASHRAE 55-81 ⇒ ASHRAE 55-92 models for the RP-462 data

files. The male samples’ model had 832 data points and was highly significant

(F=3,478; p=0), explaining 81% of variance (r = 0.90). The actual model had a

regression coefficient of 1.094 (95% confidence interval 1.086 - 1.102), indicating that

clothing insulation estimates using the new method described in ASHRAE Standard 55-

92 were, on average, 9.4% higher than those obtained for the same male subjects using

the ASHRAE 55-1981 methods.

The RP-462 female samples’ model had 1,508 data points in Figure 2.4 and was also

highly significant (F=2,330, p=0) with 61% explained variance (r = 0.78). The regression

coefficient was 1.236 (95% confidence interval 1.224 - 1.249), indicating that clothing

insulation estimates using the ASHRAE Standard 55-92 estimation method were, on

average, 23.6% higher than those obtained using the ASHRAE 55-1981 methods.



Male Clothing Estimate from RP - 462 data

ASH55-92 = 1.1979 * McIntyre2 + 0.219 * McIntyre

R2 = 0.9166

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

McIntyre (Sprauge and Munson) CLO

AS

HR

AE

55

- 92

CLO

Female Clothing Estimates from RP - 462 data

ASH55-92 = 1.0921 * McIntyre

R2 = 0.7851

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

McIntyre (Sprauge and Munson) 1980 CLO

AS

HR

AE

55

- 92

CLO

Figure 2.5: Relationships between Sprague & Munson (McIntyre 1980) and ASHRAE 55-92 clo estimates in the RP-462 field experiment

Figure 2.5 depicts the regression models fitted to the relationship between the Sprague

and Munson clo method (reported in McIntyre, 1980) and the ASHRAE 55-92 method.

The curvilinear relationship for male subjects was best approximated by a 2nd order

polynomial regression model which managed to account for 92% of the variance in

Standard 55-92 estimates (r=0.96). The female subjects in RP-462 had their ASHRAE

55-92 clothing ensemble insulation estimates were systematically larger than the

Sprague and Munson estimates by a factor of 9.2% and the linear relationship between

the two estimation methods had a correlation coefficient of r=0.89.

Male Clothing Estimates from RP-462 data

ASH55-92 = 0.3839 * ISO-842 + 0.6579 * ISO-84R2 = 0.9518

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

ISO 7730 1984 CLO

AS

HR

AE

55

- 92

CLO

Female Clothing estimates from RP-462 data

ASH55-92 = 1.057 * ISO-84R2 = 0.8476

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

ISO 7730 (1984) clo

AS

RA

E 5

5-92

clo

Figure 2.6: Relationships between ISO 7730 (ISO, 1984) and ASHRAE 55-92 clo estimates in the RP-462 field experiment

Figure 2.6 depicts the relationships between RP-462 clothing ensembles insulation

estimates using the ISO 7730 (1984) and ASHRAE 55-92 methods. The male subjects’



clothing was described by a second order polynomial regression which explained

95.2% of the variance (r=0.98). The female subjects’ model was a simple linear

regression with the ASHRAE 55-92 estimates being, on average, 5.7% higher than the

ISO 7730 (1984) estimates, and the relationship accounting for 84.8% of variance

(r=0.92).

Male Clothing Estimate from RP - 462 data

ASH55-92 = 0.3954 * ISO-942 + 0.6954 * ISO-94

R2 = 0.9448

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

ISO 7730 -1994 CLO

AS

HR

AE

55-

92 C

LO

Female Clothing Estimates from RP-462 data

ASH55-92 = 1.0049 * ISO-94

R2 = 0.8814

0.0

0.5

1.0

1.5

2.0

2.5

0.0 0.5 1.0 1.5 2.0 2.5

ISO 7730 - 1994 CLO

AS

HR

AE

55-

92 C

LO

Figure 2.7: Relationships between ISO 7730 (ISO, 1994) and ASHRAE 55-92 clo estimates in the RP-462 field experiment

Figure 2.7 above depicts the regression relationships between RP-462 clothing

ensemble insulations values estimated by the ISO-7730 2nd edition (1994) and

ASHRAE 55-92 methods. The male subjects’ clo estimates were approximated again

with a second order polynomial which accounted for 94.5% of the variance (r=0.97).

The females’ regression model was a simple linear one (r=0.94) with the ASHRAE 55-

92 clo estimates being about half a percent higher than the ISO 7730 (1994)

counterparts.

Unlike the RP-462 raw data files, the remainder of the raw data contributions to

ASHRAE RP-884’s database contained only total ensemble insulation estimates,

therefore ruling out any systematic garment-by-garment conversions and trends of the

type performed for RP-462 above. However, a preliminary questionnaire sent to each of

the RP-884 database contributors enquired about their method of clo estimation (see

Figure 2.3a and Figure 2.3b). Where the method used was pre-ASHRAE Standard 55-

1992, the original researchers’ clo estimates in their raw data file were simply scaled up

or down to equivalent Standard 55-92 levels using the conversion factors (regression

models) described above in Figures 2.4 through 2.7.



2.3.5.2. The chair insulation effect

The preliminary questionnaire on field methods sent to all database contributors (Figure

2.3a and Figure 2.3b) also enquired into whether or not the incremental insulation effect

of furniture was included in their clo estimates. If omitted from the original estimates, an

additional 0.15 clo was added, after the regression correction to clothing insulation had

been performed (McCullough and Olesen, 1994; de Dear, 1994). While it is recognised

that all chairs, stools, sofas, and any other horizontal surface which might have acted as

a chair at the time of questionnaire for the 21,000 subjects in the RP-884 database may

not have provided exactly 0.15 clo insulation at the time of interview, we feel inclusion of

this “best estimate” is preferable to omitting the effects of chairs altogether.

2.4. Developing an index for perceived thermal control

Adaptive opportunity and perceived control figured prominently throughout the literature

review in Chapter 1, but unfortunately, only a handful of original field experiments

supplied to the RP-884 database actually recorded these data in their survey buildings.

Therefore the development of a method for estimating this parameter across all

buildings within the RP-884 database was given a high priority. This section describes

the assumptions and steps we made to achieve this goal.

• Step 1: Find a data base possessing both a global perceived control item (PCC) in

the original questionnaire as well as individual items on specific adaptive

opportunities (PCEC1 through PCEC7). The ASHRAE-sponsored RP-702 (hot-

humid Townsville) and RP-821 (Cold climate Montreal) fulfilled these requirements.

• Step 2: Classification of the adaptive opportunities (PCEC variables) according to

their relevance to season (See Table 2.2). For example, access to windows was not

regarded as a relevant thermal control during winter months, whereas access to

thermostats was deemed relevant all year round.



Table 2.2: Thermal adaptive opportunities classified according to their relevance to perceived control in summer and winter seasons

PCEC variables Can You Control Season

PCEC1 windows summer

PCEC2 external doors summer

PCEC3 internal doors summer/winter

PCEC4 thermostats summer/winter

PCEC5 curtains/blinds summer/winter

PCEC6 local heaters winter

PCEC7 local fans summer

• Step 3: For the summer index, we found all cases in the database that had control

over operable windows ( PCEC1= 1) but none of the other adaptive opportunities

(PCEC2 through PCEC7). We then found the average of the overall perceived

control variable (PCC) for this subset of the database with control over windows.

Similar perceived control averages for each of the other adaptive opportunities

(PCEC variables) were obtained

• Step 4: For each of the 21, 000 subjects in the RP-884 database, we summed the

relevant perceived control scores (Step 3) for all adaptive opportunities they had at

their disposal. The aggregate score resulting from this step was entered in the

database as PCC_AG.

We can see from Table 2.3 that, in both summer and winter, the most efficacious

adaptive opportunity is “thermostats,” with “internal doors,” “curtains/blinds,” “external

doors,” “local heaters” and “local fans” all rating approximately equal for their designated

season. In summer, windows also contribute significantly to building occupants’

perceived control.



Table 2.3: Thermal adaptive opportunities scored according to their influence on perceived thermal control in summer and winter

PCEC variables Can You Control PCC score in

Summer season

PCC score in Winter

Season

PCEC1 windows 1.6

PCEC2 external doors 1.3

PCEC3 internal doors 1.3 1.3

PCEC4 thermostats 1.8 2.0

PCEC5 curtains/blinds 1.3 1.4

PCEC6 local heaters 1.3

PCEC7 local fans 1.5

2.5. Thermal acceptability issues within the RP-884 database

2.5.1 Developing a proxy variable for thermal acceptability based on thermal sensation

votes.

The thermal comfort standards such as ASHRAE’s Standard 55 and ISO 7730 are

couched in terms of maintaining certain levels of thermal acceptability within a

building. Unfortunately specific questionnaire items on thermal acceptability such as this

(TSA):

“Is this environment thermally acceptable to you at this point in time?”

were available in only a small subset of buildings in the RP-884 database, but a proxy

could be inferred from thermal sensation votes (ASH), which were recorded in all

studies. It has generally been assumed that a thermal sensation vote within the central

three categories of the ASHRAE scale is acceptable. Translating to the real-number

version (as opposed to the integer only version) of the scale, this criterion for

acceptability was defined as a thermal sensation vote falling in the interval -

1.5<ASH<+1.5. Applying this criterion to the RP-884 database, we simply tallied the

number of subjects within each building registering an acceptable thermal sensation

vote, and expressed it as a percentage of the total sample size for that particular

building (designated as fprxysat in the RP-884 codebook, Appendix E). The resulting



percentages can be treated in the meta-analysis as a thermal acceptability rating for the

building in question.

2.5.2. Rating buildings in terms of their compliance with ASHRAE Standard 55

acceptable indoor climate guidelines

If we are prepared to ignore the upper and lower humidity boundaries of the summer

and winter comfort zones depicted in ASHRAE Standard 55-92 (which may be justified

in view of the ongoing debate as to what they should actually be -- see Berglund, 1995),

it was a relatively simple task to assess each RP-884 database building’s percentage

of indoor climate measurements falling within either the summer or winter ASHRAE

comfort zone (ASH55_92). This also depends on which season the building was

surveyed in. It was done for buildings assessed during the cooling season (summer),

with:

23°C <= indoor ET* <= 26°C

and for the heating season (winter), with:

20°C <= indoor ET* <=23.5°C

The resulting percentages for each building can be regarded in the RP-884 meta-

analysis as an index of the compliance with the ASHRAE Standard 55 thermal

acceptability prescriptions.

2.6. Outdoor meteorological/climatological data for the data base

Obviously outdoor weather and climate represent key components of any conceivable

adaptive model of thermal comfort since outdoor climate partly drives acclimatization,

behavioral and psychological adaptive responses.

2.6.1. Appending outdoor weather observations to each row of data

For those studies supplied to the RP-884 database without weather data, the first

priority was to obtain meteorological data (weather data recorded on exactly the same

dates as the indoor observations). If that was not possible, then climatological data

were used (i.e. data from published sources covering long-term statistical averages for

the months in question). The outdoor atmospheric parameters collected for the RP-884

database consisted of daily outdoor air temperature and coincident relative humidity at



6:00 am and 3:00 pm. These times were selected because they represent typical times

of occurrence of daily minimum and maximum temperature. The times also typically

correspond with daily maximum and minimum relative humidity.

Various national weather and climate data resources searched and used included:

• The US National Climatic Data Center (NCDC) which currently maintains an on-line

US climatic data archive on the World Wide Web (INTERNET) from which it is

possible to download data via the Hyper-Text Transfer Protocol (HTTP).

• Commercially available CD ROMS such as the International Station Meteorological

and Climate Summary (ISMCS, 1992) proved most useful in filling some of the gaps

in the RP-884 meteorological/climatological data base.

• Published Climatological data resources such as in academic journal Weather were

used to obtain maximum and minimum temperatures (with relative humidity supplied

by ISMCS) for many of the UK field experiments.

• In two cases, meteorological data were supplied gratis by weather stations on

university campuses. These were the Radcliffe Observatory at Oxford University, UK,

and the Physical Geography Met Site at Macquarie University in Sydney Australia.

• For those investigations in which the actual outdoor meteorological data were either

unavailable from previously listed sources, or at the wrong temporal resolution, it was

necessary approach the relevant State or National Climatologists (weather bureaux)

for raw data. This was done for the Australian field experiments in Brisbane, Darwin

and Melbourne, for all the Californian field experiments, and the Steelcase project in

Michigan.

2.6.2. Climate classification applied to RP-884 raw data

A relatively simple and descriptive climate classification developed for the Macquarie

University undergraduate teaching program in climatology was applied to the RP-884

database. A map of the classification can be seen in Appendix D.



2.7. Subdivision of the standardized field experiments

Once the field experiments supplied by original researchers had been quality controlled

and standardized into the RP-884 database template, they were broken down

according to season (summer/winter) and building type (centrally controlled HVAC

buildings, naturally ventilated buildings NV, and mixed-mode buildings). Here the

distinction between centrally-controlled HVAC buildings and naturally ventilated

buildings is that in central HVAC buildings individual occupants have little or no control

over their imediate thermal environment, while occupants in naturally ventilated buildings

at least have control over operable windows. See Table 2.1 and the “sample buildings”

sections for of each project summary in Appendix C.

2.8. The meta-analysis

By aggregating the statistical unit of analysis up from the individual subject to whole

buildings, the RP-884 was able to reduce the 21,000 cases in the database to 160

buildings. This section describes how the aggregation was performed and how the

resulting meta-file was used as the basis for developing adaptive models.

2.8.1. The unit of analysis for the RP-884 meta-analysis

Earlier attempts at defining adaptive models (Humphreys and Auliciems) typically

aggregated data up to the unit of an entire field study, which often incorporated many

different buildings. Therefore the early adaptive models may have glossed over

considerable variety in contextual factors affecting subjective responses. While each

record within the RP-884 database was structured as one individual subject’s thermal

questionnaire, indoor climatic physical measurements, thermal index values and outdoor

meteorological observations, the most appropriate unit of analysis for the statistical

modelling part of the project is the single building. This level of aggregation masks

some of the inherent noise involved in a single subject’s thermal comfort assessment,

while still providing sufficient data points for statistical modelling purposes.

Furthermore, several important parameters such as neutrality and preferred temperature

can only be sensibly derived from a group’s response. Data analysis at the level of



buildings rather than individuals also ensured a modicum of consistency across several

contextual factors relevant to thermal adaptive processes, including :

• type of HVAC system,

• degree of personal environmental control,

• job satisfaction and other managerial factors that might impinge upon thermal

comfort,

• temporal variability of internal temperatures in the days/weeks preceding the thermal

comfort experiment,

• mean levels of outdoor meteorological factors and their variability in the days/weeks

preceding the comfort experiment.

In total there are 160 individual buildings in the RP-884 database.

2.8.2. Meta-file’s structure and coding conventions

The meta-file included country, city, and season in which the field experiment was

conducted. Data quality and intensity of measurement were also recorded, as was

building type (HVAC, NV, mixed-mode). Following these descriptors are means and

standard deviations of questionnaire responses (e.g. ASHRAE sensation votes and

thermal environmental measurements plus derived indices). In addition there are the

derived products such as the building’s observed thermal neutrality, preferred

temperature and thermal acceptability rating. The full listing of variables in the meta-file

and their coding conventions can be found in Appendix F.

2.8.3. General assumptions within the statistical meta-analysis

• For the purpose of statistical analysis in RP-884, field experiments with longitudinal

research design (few subjects, sampled many times) were assumed to have

independence between subjects. That is, longitudinal studies were treated the same

way as cross-sectional research designs during the meta-analysis. We also

accepted that all other statistical assumptions of linearity, normality and equality-of-

variance applied across the data base.



• For all statistical modelling conducted on the meta-file, each building “data point” was

weighted according to the number of human subjects it represented (i.e. sample size

within the building). The purpose of using a weighting factor was to minimise the

impact of outlying data points that were based on relatively small number of

observations.

• Statistical products such as building neutrality or preferred temperature were

appended as new variables in the meta file. However, if the statistical model or test

in question failed to reach statistical significance at the p=0.05 level of or better, the

building registered a missing value code for that particular variable in the meta file.

• Test statistics based on small sample sizes were interpreted with care or eliminated

(i.e. coded as “missing values”) due to their wide confidence interval estimates.

2.8.4. Statistical treatments on the various subjective thermal ratings

There are some common features in the methods used in thermal comfort field work,

particularly in relation to their assessmetns of subjective warmth within buildings. The

most common approach has been the rating scale method in which comfort is

operationalized as a vote of "neutral" or "comfortable" on scales such as those

depicted in Table 2.4. Shading has been used in the table to indicate the commonly

assumed mapping between rating scales and other thermal assessments. That is,

“neutral” is generally assumed within the comfort research community to be synonymous

with “comfortable”, “acceptable,” and “preferred.”

Despite the apparent semantic differences between the ASHRAE scale of thermal

sensation and the Bedford comfort scale, these two scales have been found to behave

more-or-less the same in most practical situations (McIntyre, 1978a; de Dear, 1985).

This encouraged direct comparisons in this project between studies using either scale.

But recent analyses of questionnaire studies in which acceptability, preference and

thermal sensation were recorded simultaneously reveal that the optimum temperature

based on thermal sensation votes does not correspond exactly with that derived from

thermal preference or acceptability (Brager, 1994). Therefore thermal acceptability and

preference were analyzed separately wherever possible.



Table 2.4: Common rating scales used in comfort research in the field

ASHRAE scale Bedford scale Acceptability Preference (McIntyre)3 hot much too warm2 warm too warm unacceptable want cooler1 slightly warm comfortably warm0 neutral comfortable acceptable no change

-1 slightly cool comfortably cool-2 cool too cool unacceptable want warmer-3 cold much too cool

The ambient temperature found by statistical analysis to most frequently coincide with

the central, usually “neutral” or “comfortable,” rating in a thermal comfort study is referred

to as that sample's "neutrality". Neutrality was calculated in the meta-analysis by the

following steps:

• Binning a particular building’s observations into half-degree (K) increments, and

working with the bins’ mean response, say thermal sensation vote, instead of

individual subjects’ thermal votes.

• Fitting a linear regression model between thermal sensations and whatever the x-axis

thermal index may be (TOP, ET, SET, PMV). The regression models weighted each

point according to the number of observations within each x-axis bin. The regression

models had the general form:

mean thermal sensation = a + b * (bin index value)

The following statistical details of each building’s four regression models (TOP, ET,

PMV, SET) were extracted for the meta-analysis:

• gradient (b) of each regression model, a measure of thermal sensitivity,

• the neutrality of the model, i.e. solution of the linear equation for a mean thermal

sensation value of zero, or “neutral,”

• the range of index values corresponding with 80% “acceptable” thermal sensations,

i.e. the distance between solutions of the linear equation corresponding with mean



thermal sensations of -0.85 (close to the mean vote of “slightly cool”) and +0.85

(close to “slightly warm”),

• The range of index values corresponding with 90% “acceptable” thermal sensations

between solutions of the linear equation corresponding with mean thermal sensations

of -0.5 and +0.5.

These boundaries were selected on the assumption that the normal distribution of

thermal sensations recorded within each building resembled that of Fanger’s PPD

function (1970). So a mean vote of ±0.85 was assumed to correspond with 80%

general acceptability (20% dissatisfaction, excluding local discomfort), ie. 80% of votes

falling inside the central three categories. A mean vote of ±0.5 was assumed to

correspond with 90% general acceptability (10% dissatisfaction).

Apart from statistically deriving observed neutralities for each building with the

procedures above, the meta-file also contains predicted neutralities for each building on

the basis of heat-balance models. The model used for this purpose was Fanger’s

(1970) PMV index, and it was applied to the problem in the following way:

• Each building’s mean values for each of the five PMV variables (TOP, RH, VEL,

INSUL, MET) were input to the WinComf© software (Fountain and Huizenga, 1996).

• The PMV model was solved iteratively by adjusting TOP (ta with tr linked) until the

PMV output field equalled zero. The final operative temperature corresponding with

PMV=0 is, by definition, predicted neutrality (PREDNEUT) for that particular building.

• One additional variable named DELTNEUT was derived from the PMV model -- the

difference between observed and predicted neutralities (NEUT_TOP and

PREDNEUT respectively). PREDNEUT was subtracted from NEUT_TOP, so that if

a particular buildings occupants were neutral in temperatures warmer than expected

by the PMV model, their DELT_NEUT was positive in sign.

2.8.5. Preferred temperatures

Preferred temperature was assessed directly (MCI) in a subset of the buildings in the

RP-884 database. The typical questionnaire item was of the type:



“At this point in time, would you prefer to feel warmer, cooler, or no change?”

These categorical data require different statistical treatments to that applied to linear

ASH scale of thermal sensation. In particular, probit analysis (Finney, 1971; Ballantyne

et al, 1977) is applicable rather than linear regression. However, probit requires binary

responses, whereas the questionnaire item described here has three possible answers.

The solution was to split the “no change” responses 50:50 into the remaining two

categories. Statistical software was applied to the task of tallying the number of

observations with MCI=1 (“want cooler”), and MCI=3 (“want warmer”) for each half-

degree temperature bin. Separate probit models were fitted to each of the “want

warmer” and “want cooler” percentages with the SAS probit procedure. Our operational

definition for the preferred temperature (or other index) within a particular building is that

value of the independent variable (e.g. operative temperature) corresponding to the

intersection of the “want cooler” and “want warmer” probit models. The fitted probit

models and preferred temperatures are depicted in separate graphs in Appendix B for

each building in which the MCI questionnaire item was available. Only those models in

which the probit models achieved statistical significance at the p=0.05 level or better

had their preferred temperatures registered in the RP-884 meta-file.

The RP-884 work statement specified separate analyses of thermal comfort (assumed

to equal sensation) and preference. Part of the logic underpinning this distinction is

known as the “semantic artefact hypothesis” which suggests that the preferred

temperature in cold climates may in fact be described as “slightly warm,” whereas

residents of hot climates may use words like “slightly cool” to describe their preferred

thermal state. While the actual temperatures preferred in both climatic extremes may in

fact be identical (assuming similar clothing, air speed, metabolism etc), the semantics

may differ to such an extent that the neutrality derived from thermal sensation scales in

the manner described above could shift up in warm climates and down in cold climates.

The RP-884 meta-file offers an opportunity to examine the semantic artefact in some

detail, since there were 55 buildings in which both thermal sensations (ASH) and

thermal preferences (MCI) were assessed. To this end, a new variable was defined in

meta-analysis:



semantic discrepancy = neutrality minus preferred temperature (°C)

discrep = neut_top - preftemp (°C)

2.9. The RP-884 database in the public domain and disseminated via the World

Wide Web

The ASHRAE RP-884 project has its own homepage on the World Wide Web at the

following URL:

http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html

The homepage, depicted in Figure 2.9, serves the purpose of introducing the RP-884

project, in particular the main team members on the project as well as the overall

concept of “adaptive models” in the context of thermal comfort research (Fig. 2.10).

The website also describes the background to the RP-884 database, linking to a flow

chart outlining the processes of data acquisition, quality control, standardization and

assimilation (Figure 2.1 is hyper-linked in the homepage). The structure of the database

and a copy of the codebook (Appendix E) are covered in another hyperlink to the

homepage. Most importantly, the comfort research community is given access to the

entire RP-884 database by means of an FTP server presented as a clickable “data

downloader” on the RP-884 website (see Figure 2.11). The table enables a total of 46

separate data files, each in a variety of formats, to be downloaded from the RP-884 host

machine in Sydney to any PC, Mac or UNIX machine elsewhere in the world, as long as

it is connected to the internet. Several data formats are available in an effort to facilitate

cross-platform transfers, but the most heavily used format is MS Excel® V.5

spreadsheets for use within the MS Windows ® 3.X or Windows 95 operating

environments. These data files have been “zipped” into compact, self-extracting

archives with *.exe filenames. The user will need to execute (run) the *.exe file after

it has been transferred and it will automatically inflate back to the native Excel® 5 format,

ready for use on the user’s machine with an *.XLS filename. The forenames of the 46

files within the “data downloader” correspond to the file numbers listed in Table 2.1

earlier in this chapter.



Figure 2.9: The homepage for RP-884 on the World Wide Web http://atmos.es.mq.edu.au/~rdedear/ashrae_rp884_home.html



Figure 2.10: One of the pages linked to the ASHRAE RP-884 homepage



Figure 2.11: The entire RP-884 database (46 data files) is accessible to anyone who is interested via

this “data downloader. The device is a “clickable form” interface and can be found on the RP-884 project’s website.



2.10. Summary of the methods used in RP-884

This chapter has described the RP-884 approach to developing adaptive models of

thermal comfort and preference. Underpinning the method has been the creation of a

large database of thermal comfort field research observations. The raw data for this

database were assembled from a wide variety of climatic, geographic and architectural

contexts, and at last count, the database had in excess of 21,000 rows of raw data. The

database is made available to the thermal comfort R&D community via a homepage on

the World Wide Web.

The raw data supplied to the RP-884 database included basic characteristics of the

building in which each subject was interviewed, demographic descriptors, the subject’s

thermal sensation, preference and acceptability votes at the time of the indoor climate’s

physical measurements (ta, tr, rh, v, Tu, clo, met). In view of the significance of clothing

in terms of behavioural thermal adjustments and also various thermal index calculations,

particular care was taken to ensure clothing and furniture insulation values were derived

from, or converted to, a consistent estimation method throughout the entire RP-884

database -- the ASHRAE Standard 55-92 method was selected as the benchmark for

this purpose.

Once raw data had been standardized, cleaned and assimilated into the database,

thermal indices such as ET*, SET, PMV, PPD, PD were calculated using a standard

software tool (ASHRAE RP-781). In addition, outdoor meteorological and

climatological observations were appended to each set of data in the database to

enable an examination of the role played by outdoor atmospheric environmental factors

in thermal adaptation.

The meta-analysis for RP-884 was conducted on this large database by aggregating

observations up to the level of individual buildings, of which there were 160 in total.

Statistical results were derived at this level of aggregation, including the building’s

thermal neutrality, preferred temperature, thermal acceptability rating, mean indoor



thermal index values, as well as mean outdoor climatic indices at the time of the

building’s survey.

The next chapter (3) describes the basic results of this meta-analysis. These then

provide the foundation upon which adaptive models of thermal comfort will be built in

Chapter 4.


Basic Results page MRL Australia 67

CHAPTER 3 - BASIC RESULTS

The generic term “thermal comfort” covers many aspects of subjective thermal

experience, each of which has been operationalized with specific questionnaire items

by researchers over the years. This chapter focuses on three specific dimensions of

thermal perception: a) thermal sensation, b) thermal acceptability, and c) thermal

preference. The chapter relates these subjective data to; a) indoor climate, b) outdoor

climate, and c) various built environmental-contextual factors.

3.1. Interactions with Indoor Climate

This section examines thermal perceptual data in relation to indoor climatic parameters.

The first subsection deals with thermal sensation (ASH) and derived parameters such as

thermal neutrality and their statistical associations with indoor climatic factors. The second

subsection examines thermal acceptability, either directly measured or inferred from (ASH),

while the third subsection examines thermal preferences.

3.1.1. Thermal sensation

Thermal sensation (ASH) within each building was analyzed separately with respect to four

indices of indoor warmth:

• operative temperature (TOP)

• new effective temperature (ET)

• predicted mean vote (PMV)

• standard effective temperature (SET)

As described in the Methods Chapter (2), data were binned into half-degree steps for the

thermal indices. Simple linear regression models within each building were fitted to these

binned ASH data as follows:

mean thermal sensation = a + b * (bin index value)

The regression models generated within the SAS® software package weighted each data

point (bin) according to the number of observations it represented; the purpose being to



minimize the impact of outlying data points that were based on relatively small number of

observations.

The resulting models and their associated statistics have been plotted building-by-building in

Appendix A. Of the 160 separate building analyzed, only results from models that achieved

a statistical significance level (T test) of 95% or better were extracted for use in this

chapter’s meta-analysis.

3.1.1.1. Dependence of thermal sensation on indoor operative temperature

Regression of binned mean thermal sensation (ASH) on indoor operative temperature

(TOP) was performed building-by-building. Individual graphs of the models can be found in

Appendix A. Of the total 160 models fitted, 99 (out of 157 buildings, with 3 missing values)

achieved statistical significance at the 95% confidence level. Table 3.1 below summarises

the significant regression models.

Table 3.1: Summary of the weighted linear regression of bin mean thermal sensation on indoor operative temperature (°C).

centrally heated/air-conditioned buildings

naturally ventilated buildings

mixed-mode buildings

number of buildings

109 (2 missing values)

44 (1 missing value)

4 (no missing

values) number of buildings with regression models achieving 95% significance

63 (57.8% of total)

36 (81.8% of total)

3 (75.0% of total)

mean (±stdev) model constant (y-intercept)*

-11.96 (±5.839)

-6.65 (±3.572)

-8.65 (±2.982)

mean (±stdev) model gradient* 0.51 (±0.248)

0.27 (±0.134)

0.39 (±0.105)

* Based on those models (y=a + b*TOP) achieving 95% statistical significance or better

The relatively small number of central HVAC buildings listed in Table 3.1 as producing a

significant regression model is probably related to the relatively small number of temperature

bins (independent variable), found within such buildings (i.e. tightly controlled temperatures).



Naturally ventilated buildings, on the other hand, provided significant regression equations in

four out of every five cases.

The last row of Table 3.1 indicates that occupants of centrally air-conditioned buildings had

thermal sensations that were approximately twice as sensitive to indoor operative

temperatures as those of occupants of naturally ventilated buildings. On average, mean

thermal sensations changed one unit every two degrees of operative temperature in centrally

air-conditioned buildings, whereas in naturally ventilated buildings, a four degree change

was needed to shift mean thermal sensations jump by one unit. Testing this difference in

sensitivity with the T statistic indicated it was significant (T=5.37, df=97, p<0.001).

3.1.1.2. Dependence of thermal sensation on indoor ET

Regression of binned mean thermal sensation (ASH) on indoor new effective temperature

(ET) was performed building-by-building and individual graphs of the models are presented

in Appendix A. Of the total 160 weighted models fitted, 98 (out of 157, minus 3 missing

values) achieved statistical significance at the 95% confidence level. Table 3.2 below

summarises the broad findings.

Table 3.2: Summary of the weighted linear regression of bin mean thermal sensation on indoor effective temperature (°C).




number of buildings



4

number of buildings with regression models achieving 95% signif.

64

(58.7% of total)

34

(77.3% of total)

3

(75.0% of total) mean (±stdev) model constant (y-intercept)*

-11.81 (±6.383)

-6.76 (±3.076)

-7.94 (±1.884)

mean (±stdev) model gradient *

0.50 (±0.273)

0.28 (±0.126)

0.37 (±0.075)

* Based on those models (y=a + b*ET) achieving 95% statistical significance or better

As was found with the operative temperature index in Table 3.1, thermal sensations were

approximately twice as sensitive to ET in centrally conditioned buildings compared to

naturally ventilated buildings (T=4.45, df=96, p<0.001). This suggests that the difference



between classes of building was not simply a result of humidity effects being ignored by the

operative temperature index of indoor climate.

3.1.1.3. Dependence of thermal sensation on PMV

Regression of binned mean thermal sensation (ASH) on indoor Predicted Mean Vote

(PMV) was performed building-by-building and individual graphs of the models are

presented in Appendix A. Of the total 160 weighted models fitted, 60 (out of 159, minus 1

missing value) achieved statistical significance at the 95% confidence level. Table 3.3

below summarises the broad findings.

Table 3.3: Summary of the weighted linear regression of bin mean thermal sensation on indoor Predicted Mean Vote (7-pt scale).




number of buildings

111 (no missing values)



number of buildings with regression models achieving 95% signif.

33

(29.7% of total)

28

(63.6% of total)

3

(75.0% of total) mean (±stdev) model constant (y-intercept)*

0.06 (±0.274)

-0.04 (±0.611)

0.68 (±0.697)

mean (±stdev) model gradient*

0.74 (±0.271)

0.62 (±0.329)

0.65 (±0.195)

* Based on those models (y=a + b*PMV) achieving 95% statistical significance or better

Since the units of the PMV index and ASHRAE thermal sensation scale are one and the

same, one would expect the gradient of the regressions models in Table 3.3 above to have

been unity, on average. As indicated by the mean gradients in Table 3.3, however, actual

mean thermal sensations appear to be less sensitive than PMV predicted, although in the

case of centrally conditioned buildings, the observations were about 75% of expectation.

This ratio dropped to about 60% in naturally ventilated buildings, but the difference between

HVAC and NV was not statistically significant (T=1.56, df=59, p>0.1).

The fact that the difference in thermal sensitivity between air-conditioned and naturally

ventilated buildings dropped from the 2:1 ratio that was observed with the simpler thermal

indices of operative and effective temperature suggests that other physical and behavioral

factors were affecting the human body heat balance equation, especially in the naturally



ventilated context. Factors such as clothing insulation and air speed, that are excluded from

the simpler indices but incorporated in the PMV calculations, might explain some of the

relationship between thermal sensations and indoor temperatures. That is, occupants within

naturally ventilated buildings were more thermally adaptable at manipulating their heat

balance than their counterparts in centrally conditioned buildings.

3.1.1.4. Dependence of thermal sensation on indoor SET

Regression of binned mean thermal sensation (ASH) on indoor standard effective

temperature (SET) was conducted building-by-building across the RP-884 database and

individual graphs of the models are presented in Appendix A. Of the total 160 weighted

models fitted, 56 (out of 152, 8 missing values) achieved statistical significance at the 95%

confidence level. Table 3.4 below summarises the broad findings.

Table 3.4: Summary of the weighted linear regression of bin mean thermal sensation on indoor standard effective temperature (°C).

centrally heated/air-conditioned

buildings



number of buildings



3 (1 missing value)

number of buildings with regression models achieving 95% signif

32

(30.2% of total)

27

(62.8% of total)

3

(100% of total) mean (±stdev) model constant (y-intercept)*

-5.06 (±3.42)

-4.40 (±2.04)

-4.66 (±2.23)

mean (±stdev) model gradient*

0.21 (±0.14)

0.18 (±0.08)

0.21 (±0.06)

* Based on those models (y=a + b*SET) achieving 95% statistical significance or better

Table 3.4 indicates that regression gradients were relatively constant across all classes of

building at about one thermal sensation unit for each five degrees of environmental

temperature. The same interpretation that we applied to the PMV index (preceding section)

is relevant here as well -- namely that heat balance factors included in these more complex

indices such as PMV and SET can account for the greater degree of thermal adaptability in

naturally ventilated buildings compared to centrally conditioned buildings.



3.1.2. Thermal neutrality

The term “thermal neutrality” refers to a specific value of the indoor thermal environmental

index (e.g. operative temperature) corresponding to a mean thermal sensation vote of zero

on the seven-pt scale (i.e.“neutral”). Neutrality is readily obtained by solving each building’s

regression equation for y=0.

3.1.2.1. Neutral operative temperatures (neut_top)

Solution of the regression equations for the “neutral” sensation in relation to the indoor

operative temperature (top) was performed building-by-building. Table 3.5 below

summarises the neut_top statistics from 160 buildings.

Table 3.5: Summary of the neutral operative temperatures (neut_top) from 160 buildings in the database (°C).




number of signif results in summer building sample*

47 out of 78

(I missing value)

27 out of 32

(1 missing value)

1 out of 2

(no missing values)

mean neut_top (±stdev) in summer sample °C

24.1 (±1.31)

24.6 (±2.42)

23.9 (±0)

number of signif results in winter building sample*

14 out of 30 (2 missing values)

7 out of 11 (1 missing value)

2 out of 2 (no missing values)

mean neut_top (±stdev) in the winter sample °C

22.5 (±0.35)

22.4 (±2.78)

20.7 (±0.50)

* only results from buildings with statistically significant regression models used

Table 3.5 indicates that thermal neutrality was observed across the winter experiments in

RP-884 database at an average indoor operative temperature of about 22.5°C, regardless

of whether the buildings were centrally conditioned or naturally ventilated. However, the

standard deviation of winter neutralities in the sample of naturally ventilated buildings was

about eight times that observed in the centrally conditioned buildings. In the summer field

experiments there was a tendency for neutrality to be half a degree warmer in naturally

ventilated buildings compared to centrally conditioned, but the difference failed to reach



significance (T = 1.16, df = 72, p > 0.1). The standard deviation of neutralities was again

found to be larger in the summer sample of naturally ventilated buildings.

All Buildings

15

17

19

21

23

25

27

29

18 20 22 24 26 28 30 32

mean indoor operative temperature (oC)

neut

ral o

pera

tive

tem

pera

ture

(oC

)

neut_top = 15.34 + 0.35 * top

R2 = 0.38, p = 0.0001

Central HVAC and Mixed Mode Buildings

15

17

19

21

23

25

27

29

18 20 22 24 26 28 30 32


neut

ral o

pera

tive

tem

pera

ture

(oC

)

neut_top = 8.92 + 0.62 * top

R2 = 0.27, p = 0.0001

Naturally Ventilated Buildings

15

17

19

21

23

25

27

29

18 20 22 24 26 28 30 32


neut

ral o

pera

tive

tem

pera

ture

(oC

)

neut_top = 15.47 + 0.35 * top

R2 = 0.32, p = 0.0013

Figure 3.1: Dependence of neutral indoor temperature on buildings’ mean temperature

The adaptive hypothesis predicts that the temperatures regarded as “neutral” within any

particular building will depend, in part, on the level of warmth typically encountered and

expected within that building. Figure 3.1 expresses this idea statistically by regressing the

neut_top observations against mean indoor operative temperatures. Figure 3.1 lends



support to this adaptive hypothesis insofar as the models show a moderate linear correlation

(r=0.5~0.6). The main regression equation (“all buildings” in Figure 3.1) indicates that

indoor neutrality increases by about one degree (°C) for every three degrees increase of

indoor temperature. Despite the apparent difference in gradients between the HVAC and

NV building samples, their regression gradients were not significantly different (T=0.08,

df=96, p>0.5).

3.1.2.2. Neutral effective temperatures (neut_et)

Solution of the regression equations for the “neutral” sensation in relation to ET was

performed building-by-building across the RP-884 database. Table 3.6 below summarises

the neut_et findings from 160 buildings. Effective temperature neutralities within the

naturally ventilated buildings were, on average, about half a degree warmer than their

centrally conditioned counterparts in both seasons, but the difference was insignificant

(summer T = 1.12, df = 70, p > 0.2 and winter T = 0.99, df = 21, p > 0.2).

Table 3.6: Summary of the neutral effective temperatures (neut_et) from 160 buildings in the database (°C).




number of buildings in summer sample*

45 out of 78

(1 missing value)

27 out of 31

(2 missing values)

1 out of 2

(no missing values)

mean neut_et (±stdev) in summer sample °C

24.0 (±1.63)

24.6 (±2.91)

23.4 (±0)

number of buildings in winter sample*

16 out of 30 (2 missing values)

7 out of 11 (1 missing value)

2 out of 2 (no missing values)

mean neut_et (±stdev) in the winter sample °C

22.4 (±0.77)

22.9 (±1.68)

20.7 (±0.55)


3.1.2.3. Neutral predicted mean votes (neut_pmv)

Solution of the regression equations for the “neutral” sensation in relation to the PMV index

was done building-by-building. Since both dependent and independent variables are based



on the same scales, the static heat-balance model theory predicts that subjects would vote

zero (neutral) when they were in heat-balance conditions corresponding to PMV=0.

However, results of these regression analyses indicate significant departures from this

expectation. Table 3.7 below summarizes the neut_pmv findings from 160 buildings in the

RP-884 meta-analysis. Many buildings in the database failed to produce statistically

significant models when using the PMV index, but those that did indicated that occupants of

HVAC buildings in summer found neutrality in indoor climatic conditions that corresponded

with PMV=0; i.e. model matched observation quite closely. On the other hand, occupants of

naturally ventilated buildings during summer found themselves feeling neutral in indoor

conditions that the PMV model indicated as cooler-than-neutral. This HVAC v NV difference

however, proved to be statistically insignificant (T = 1.48, df = 47, p > 0.1) due to the small

number of buildings being compared.

Table 3.7: Summary of the neutral Predicted Mean Votes neut_pmv from 160 buildings in the database (7-pt scale).





23 out of 79

(no missing values)

26 out of 32 (1 missing)

1out of 2

(no missing values)

mean neut_pmv (±stdev) in the summer sample

0.01

(±0.32)

-0.43

(±1.40)

-0.24±0)


7 out of 32

(no missing values)

1 out of 12

(1 missing value)

1 out of 2

(no missing values) mean neut_pmv (±stdev) in the winter sample

-0.55

(±0.301)

1.11 (±0)

-0.53 (±0)


3.1.2.4. Predicted neutralities with the PMV heat balance model

As indicated in the preceding table, neutrality in several buildings occurred in thermal

environmental conditions that departed significantly from the predictions of the static heat-

balance models of thermal comfort. Another way of examining this issue is to use the PMV



model to actually predict the neutral operative temperature for each building, ceteris paribus.

This new variable in the meta-analysis was code-named PREDNEUT.

All buildings

10

12

14

16

18

20

22

24

26

28

30

12 14 16 18 20 22 24 26 28 30 32


pre

dn

eu

t (o C

)

predneut = 15.43 + 0.33 * top

R2 = 0.54, p = 0.0001

Central HVAC and Mixed Mode buildings

10

121416

1820

2224

262830

12 14 16 18 20 22 24 26 28 30 32


pre

dn

eu

t (o C

)

predneut = 13.16 + 0.43 * top

R2 = 0.15, p = 0.0001

Naturally Ventilated buildings

10

1214

16

1820

22

2426

28

30

12 14 16 18 20 22 24 26 28 30 32


pre

dn

eu

t (o C

)

predneut = 15.07 + 0.34 * topR2 = 0.70, p = 0.0001

Figure 3.2: Dependence of neutrality predicted by the PMV heat-balance model (PREDNEUT) on mean indoor temperatures (TOP).

The clear dependence of predneut on mean indoor top in Figure 3.2 suggests that the other

heat balance variables that change in response to indoor temperature -- such as clothing

insulation and air speeds, were driving the PMV predicted neutralities. The correlation

appears to be strongest in the case of the naturally ventilated buildings (r=0.84). In the case



of the HVAC and mixed mode buildings in Figure 3.2, the seven outlying buildings below the

fitted regression line were from the Brown (1992/3) study in industrial settings where

metabolic rates were elevated well above those encountered in the remaining office and

residential buildings, causing the predicted neutrality to be depressed by as much as 10°C

below the trendline. These outliers account for the reduction in correlation coefficients.

3.1.2.5. Neutral standard effective temperatures (neut_set)

Solution of the regression equations for the “neutral” sensation in relation to SET was

performed building-by-building. Table 3.8 below summarises the neut_set findings from

160 buildings.

Table 3.8: Summary of the neutral standard effective temperatures (neut_set) (°C).





24 out of 73

(6 missing values)

25 out of 31

(2 missing values)

1 out of 1

(1 missing value)

mean neut_set (±stdev) in the summer sample

24.5

(±1.51)

24.1

(±2.85)

23.8 (±0)


8 out of 30

(2 missing values)

2 out of 11

(1 missing value)

2 out of 2

(no missing values)

mean neut_set (±stdev) in the winter sample

25.2

(±3.51)

31.3

(±1.89)

19.6

(±6.73)


Less than one third of the centrally heated/air-conditioned buildings in the sample yielded

significant regression models, probably because of the restricted range of thermal

environmental conditions in such buildings. Naturally ventilated buildings, with their greater

internal climatic variety produced a significant regression equation against the SET index in

the majority of cases. In those buildings sampled during the summer season, there was an

average neutral SET in the 24~24.5°C region and the difference of 0.4 K between the



means of naturally ventilated and centrally heated/air-conditioned buildings was not

statistically significant (T = 0.61, df = 47, p > 0.5). The anomalously high average neutral

SET of 31.3°C observed in naturally ventilated buildings in winter was based on only two

buildings. The seasonal difference in neutrality for HVAC buildings was not statistically

significant (T = 0.8, df = 30, p > 0.2).

3.1.3. Thermal acceptability and indoor climate

Thermal acceptability was directly assessed in a small subset of studies in the RP-884

database, but it could be inferred from thermal sensation votes, which were recorded in all

studies. This section analyzes both direct and inferred versions of thermal acceptability

response.

3.1.3.1. Relationship between direct and inferred thermal acceptability

Testing the assumption that a thermal sensation vote falling in the interval -1.5<ASH<+1.5

equated with thermal acceptability was possible by comparing frequencies of both direct

and indirect thermally acceptable votes within each building. Each building’s frequency of

directly assessed acceptable votes was coded as f_tsa_2 and the frequency of acceptable

thermal sensations was coded as prxy_tsa in the meta-analysis.

The result have be depicted in Figure 3.3. The graphs represent weighted regression

models of prxy_tsa versus f_tsa_2. Each point in the graphs represents a specific building

in the database. The solid line plotted through the data points represents the expected

relationship (gradient 1:1) whereas the dotted line represents the line of best fit (with model

equation and statistics annotated on each graph).



Townsville Australia RP-702 (tropical dry season), HVAC buildings.

prxy_tsa = 0.55 * tsa + 38.849

R2 = 0.4685

60

70

80

90

100

60 70 80 90 100

TSA (% aceptable)

Pro

xy T

SA

(%

"sa

tisfa

ctor

y" A

SH

vot

es)

Townsville Australia RP-702(tropical wet season), HVAC buildings.

prxy_tsa = 0.4288 * tsa + 46.302

R2 = 0.2038

60

70

80

90

100

60 70 80 90 100

TSA (% acceptable)

Pro

xy T

SA

(%

"sa

tisfa

ctor

y" A

SH

vot

es)

Montreal Canada RP-821 (summer), HVAC buildings.

prxy_tsa = 0.5007 * tsa + 32.536

R2 = 0.4752

50

60

70

80

90

100

50 60 70 80 90 100

TSA (% acceptable)

Pro

xy T

SA

(%

"sa

tisfa

ctor

y" A

SH

vot

es)

Montreal Canada RP-821 (winter), HVAC buildings.

prxy_tsa = 1.3448 * tsa - 35.093

R2 = 0.7155

50

60

70

80

90

100

50 60 70 80 90 100

TSA (% acceptable)

Pro

xy T

SA

(%

"sa

tisfa

ctor

y" A

SH

vot

es)

Sydney Australia (summer and winter) HVAC and Mixed buildings.

prxy_tsa = -0.2419 * tsa + 92.27

R2 = 0.9872

60

70

80

90

100

60 70 80 90 100

TSA (% acceptable)

Pro

xy T

SA

(%

"sa

tisfa

ctor

y" A

SH

vot

es)

proxy acceptability versus actual acceptability for all available buildings

30

40

50

60

70

80

90

100

30 40 50 60 70 80 90 100

TSA (% acceptable)

Pro

xy T

SA

(% "

satis

fact

ory"

AS

H v

otes

)

prxy_tsa = 30.74 + 0.61 * tsa

R2 = 0.48, p = 0.0001

Figure 3.3: Comparison of directly determined and inferred thermal acceptability. Each data point represents an individual building from the RP-884 database.

Figure 3.3 indicates that the strength of association between direct and inferred thermal

acceptability varied considerably across field experiments. For the pooled analysis of all

seven experiments’ buildings (top left panel in Figure 3.3), about half of the variance in

acceptable thermal sensations (prxy_tsa) could be accounted for by the direct thermal

acceptability ratings. Discounting Rowe’s (1996) Sydney experiment due to its small

sample size (three building data points), the highest correlation was found in Donnini’s

(1996) Montreal winter experiment (r = 0.85). For the remaining experiments, the

correlations can be described as moderate. In five out of the seven individual project graphs



depicted in Figure 3.3, the gradient of the observed dependence of inferred acceptability on

directly stated acceptability was significantly lower than the unity we expected. Subjects

apparently were voting that thermal sensations outside the central three categories of the

ASHRAE 7-pt scale were still acceptable. Expressing that a different way, buildings that

had a high rating of thermal acceptability on the direct scale (>80%), typically scored lower

acceptability ratings on the basis of percentage of thermal sensations falling within the

central three categories of the 7-pt scale.

3.1.3.2. Directly determined thermal acceptability.

As noted in the preceding section, “acceptable” TSA votes (“At the present time, is this

thermal environment acceptable to you or not?”) were tallied for each building and

expressed as a percentage of all responses in the building. This percentage was coded as

f_tsa_2 for each building in the RP-884 meta-analysis.

Ignoring the upper and lower humidity boundaries of the summer and winter comfort zones

depicted in ASHRAE Standard 55-92, the percentage of indoor climate measurements

within each RP-884 database building complying with the relevant summer or winter

ASHRAE comfort zone ET boundaries was coded as ASH55_92 in the meta-analysis. A

simple assessment of the practical utility of the ASHRAE comfort zones can be performed

by comparing these acceptability levels predicted from indoor climatic measurements

(ASH55_92) with the corresponding thermal acceptability ratings for each building. These

comparisons have been performed in Figures 3.4 and 3.5 respectively -- each data point in

the graphs represents a single building.



Thermal Acceptability (all buildings)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

% indoor climates falling within ASHRAE 55-92 comfort zones

TS

A (

% a

ccep

tabl

e)

tsa = 75.05 + 0.03 * ASH55_92

R2 = 0.01, p = 0.5258

Figure 3.4: Relationship between direct thermal acceptability ratings of buildings (f_tsa_2) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92).

Thermal Acceptability (all buildings)

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

% indoor climates falling within ASHRAE 55-92 comfort zones

Pro

xy T

SA

(%

vot

es -

1.5

< a

shra

e <

1.5

)

prxy_tsa = 70.96 + 0.16*ASH55_92

R2 = 0.14, p = 0.0001

Figure 3.5: Relationship between acceptability thermal sensation ratings of buildings (prxy-tsa) and buildings’ compliance with ASHRAE Standard 55-1992 comfort zone ET boundaries (ASH55_92).

Regardless of which thermal acceptability measure was adopted, Figures 3.4 and 3.5

indicate that compliance with the ET prescriptions of ASHRAE Standard 55-1992 had

little or no bearing on the buildings’ acceptability ratings by occupants. This is indicated



clearly by the complete lack of statistical significance in the regression models plotted on the

graphs in Figures 3.4 and 3.5. A logical extension of this null result is that most of the

buildings which had very low levels of compliance with ASHRAE 55-92 (say, ASH55_92 <

30%) still had occupant ratings of thermal acceptability better than 60 to 70%

TSA versus TOP for all buildings

0

20

40

60

80

100

18 20 22 24 26 28 30 32 34


TSA

(% a

ccep

tabl

e)

tsa = -287.56 + 27.75 * top - 0.52 * top2

R2 = 0.08, p = 0.0883

TSA versus ET for all buildings

0

20

40

60

80

100

18 20 22 24 26 28 30 32 34

mean indoor effective temperature (oC)

TSA

(% a

ccep

tabl

e)

tsa = -253.80 + 25.64 * et - 0.49 * et 2

R2 = 0.06, p = 0.1554

TSA versus SET for all buildings

0

20

40

60

80

100

18 20 22 24 26 28 30

mean indoor standard effective temperature (oC)

TSA

(% a

ccep

tabl

e)

tsa = -724.32 + 61.02 * set - 1.16 * set 2

R 2 = 0.22, p = 0.0005

TSA versus PMV for all buildings

0

20

40

60

80

100

-3 -2 -1 0 1 2 3

mean indoor predicted mean vote

TSA

(% a

ccep

tabl

e)

tsa = 77.23 + 16.00 * pmv - 11.63 * pmv2

R2 = 0.18, p = 0.0030

Figure 3.6: Dependence of direct thermal acceptability ratings on mean thermal index values. Each data point represents a building.

Figure 3.6 shows the percentage of occupants within each building voting “acceptable”

(f_tsa_2) as a function of the mean indoor climatic index values recorded for each building.

The indices selected for this analysis covered the spectrum from relatively simple operative

temperature up to fully developed heat balance indices such as PMV and SET. The

expected relationship between percentage satisfied and indoor warmth is hyperbolic,

peaking around the database’s mean neutrality or preferred temperature. Unfortunately the

majority of buildings available for the analysis were clustered within a fairly narrow band of

indoor temperatures, centred on 23°C, and so the data are not well suited to regression

analysis. As a result, the weighted 2nd order polynomial models fitted to the TOP and ET



indices in Figure 3.6 were not statistically significant. While the models fitted for the more

sophisticated heat balance models such as PMV and SET did achieve statistical

significance, the explained variance was about 20% in both cases.

The underlying concept of Fanger’s Predicted Percentage Dissatisfied index (1970) is

simply that as mean indoor climatic conditions depart from the optimum (assumed to be

PMV=0), the percentage of persons experiencing unacceptable thermal sensations

increases. Despite the obvious lack of normality in statistical distributions for TSA % across

the RP-884 building database, the 2nd order polynomial regression equation fitted to mean

building PMV values in Figure 3.6 is of the hyperbolic form suggested by the PPD concept.

The fact that the PMV index produced a statistically significant relationship (R2= 0.18) where

the simpler indices of TOP and ET failed suggests that the inclusion of other heat balance

factors such as air speed, metabolic rate and clothing actually does what it’s supposed to do

-- improve predictions. The same interpretation can be applied to the SET index, since it

too incorporates the full array of heat balance variables, and as seen in Figure 3.6, its 2nd

order polynomial model was also statistically significant (p=0.0005).

3.1.3.3. Thermal acceptability inferred from thermal sensation.

Fanger’s Predicted Percentage Dissatisfied (PPD) model is premised on the assumption

that a thermal sensation vote within the central three categories of the ASHRAE 7-point

scale (slightly cool + neutral + slightly warm) is acceptable and satisfactory. Since it is

derived from the core thermal response item of ASH, this proxy for thermal acceptability was

obtained for every respondent in the cumulative ASHRAE RP-884 database (n>21,000) and

coded as prxy_tsa.



Proxy TSA versus TOP for all buildings

0

20

40

60

80

100

10 15 20 25 30 35


Pro

xy T

SA

(% a

ccep

tabl

e)

prxy_tsa = -176.48 +20.56 * top - 0.41 * top2

R2 = 0.49, p = 0.0001

Proxy TSA versus ET for all buildings

0

20

40

60

80

100

10 15 20 25 30 35


Pro

xy T

SA

(% a

ccep

tabl

e)

prxy_tsa = -171.19 + 20.21 * et - 0.40 * et 2

R2 = 0.51, p = 0.0001

Proxy TSA versus SET for all buildings

0

20

40

60

80

100

14 16 18 20 22 24 26 28 30 32 34

mean indoor standard effective temperature (oC)

Pro

xy T

SA

(% a

ccep

tabl

e)

prxy_tsa = -679.20 + 56.70 * set - 1.05 * set 2

R 2 = 0.41, p = 0.0001

Proxy TSA versus PMV for all buildings

0

20

40

60

80

100

-3 -2 -1 0 1 2 3

mean indoor predicted mean vote

Pro

xy T

SA

(% a

ccep

tabl

e)

prxy_tsa = 83.72 + 10.75 * pmv - 10.52 * pmv2

R2 = 0.42, p = 0.0001

Figure 3.7: Dependence of building acceptability ratings (derived from thermal sensation) on mean thermal index values. Each data point represents a building.

As noted in the preceding section, the majority of buildings in the RP-884 database were

clustered within a narrow range of mean indoor temperatures, severely limiting the scope for

regression analyses. However in Figure 3.7, because of the larger sample size compared

with the preceding section, all four indoor climatic indices showed statistically significant

relationships with this proxy building thermal acceptability index.

3.1.3.4. Thermal sensitivity and the range of thermally acceptable temperatures.

Given the relatively weak correlations for thermal acceptability in the preceding sections, the

use of the associated regression models to define acceptable ranges of thermal indices

would be not very reliable. A more feasible alternative, based on Fanger’s Predicted

Percentage Dissatisfied (PPD) concept (1970), can be applied to this question of

acceptable ranges. As noted earlier, PPD is a function of mean thermal sensation (PMV in

Fanger’s terminology) and a PMV of ±0.85 is assumed to correspond with 80%

acceptability. Logically therefore, assuming that actual thermal sensation votes (ASH) are

distributed around their mean with a similar variance as predicted votes are (PMV/PPD),

the values of a particular indoor thermal index (e.g. TOP, ET, PMV or SET) corresponding



with mean ASH votes of ±0.85 can be interpreted as the limits of acceptable thermal

environments (for 80% acceptability). This derivation of acceptable ranges was

operationalized by solving the ASH linear regression models (Appendix A) that we defined

for each of the main indoor thermal indices (TOP, ET, PMV, SET) for each of the buildings

in the RP-884 database, using ASH=-0.85 and again using ASH=+0.85. Subtraction of the

index value, say TOP, corresponding with -0.85 from the corresponding +0.85 value defines

the width of 80% acceptable TOP values for that particular building and the variable thus

defined was codenamed RANG_TOP in the meta-analysis.

Note that acceptable temperature ranges using these techniques were only feasible in

buildings whose ASH regression models (Appendix A) achieved statistical significance at

the 95% confidence level.

Table 3.9: Range of acceptable operative temperatures (Kelvin).


buildings



number of buildings 108 (3 missing values)



number of buildings with regression models achieving 95% significance*

62

(57% of total)

33

(75% of total)

3

(75% of total)

80% acceptability criterion (RANG_TOP) Mean (±stdev)

4.1

(±1.91)

6.9

(±2.79)

4.5

(±1.24) 90% acceptability criterion (RANTOP10) Mean (±stdev)

2.4

(±1.12)

4.9

(±3.27)

2.7

(±0.73)

* Based on those thermal sensation models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better

The 80% acceptable range of operative temperatures was, on average, 6.9 K wide in

naturally ventilated buildings, which was about 70% wider than in centrally heated/air-

conditioned buildings. This difference was statistically significant (T = 5.69, df = 93,

p<0.001). The acceptable range of operative temperatures for mixed mode buildings was,



on average, between that of HVAC and NV buildings, but the small number of cases

precludes any statistical tests.

Also included in Table 3.9 are the acceptable operative temperature ranges for a more

stringent criterion of 90% acceptability (labelled RANTOP10 in the meta-anlysis). These

were derived from each building’s thermal sensation v operative temperature regression

equation, but instead of solving for neutrality ± 0.85 sensation units (as was the case for the

80% criterion used for RANG_TOP), we applied the PPD=10% assumption, namely

neutrality ± 0.5 sensation units. The acceptable ranges in Table 3.9 reduced from 4.1 K for

80% acceptability in HVAC buildings to 2.4 K using the 90% acceptability criterion (not

dissimilar to the prescriptive ranges found in ASHRAE Standard 55-92 for the same

acceptability criterion for general thermal comfort, excluding local discomforts). However,

the average 90% acceptability range observed for RP-884’s naturally ventilated sample was

twice as wide as observed in the HVAC sample in Table 3.9 (and also prescribed in

ASHRAE Standard 55-92).



All Buildings

0

2

4

6

8

10

12

14

17 19 21 23 25 27 29 31 33


ran

g_

top

(K

)

rang_top = 61.87 - 4.58 * top + 0.09 * top2

R2 = 0.18, p = 0.0001


0

2

4

6

8

10

12

14

17 19 21 23 25 27 29 31 33mean indoor operative temperature (oC)

ran

g_

top

(K

)

rang_top = 30.51 - 1.82 * top + 0.04 * top2

R2 = 0.02, p = 0.7189


0

2

4

6

8

10

12

14

17 19 21 23 25 27 29 31 33


ran

g_

top

(K

)

rang_top = 145.35 - 11.85 * top + 0.25 * top2

R2 = 0.14, p = 0.0099

Figure 3.8: Dependence of the acceptable range of operative temperature within buildings on mean operative temperature indoors

Figure 3.8 indicates a loose relationship between acceptable ranges and mean indoor

operative temperatures (RANG_TOP) -- as a building’s mean indoor temperature deviates

from moderate levels in the vicinity of 24~25°C, the acceptable range tends to increase.

Conducting this analysis separately on the HVAC and NV buildings indicates a lack of any

statistical relationship for the sample of naturally ventilated buildings.




0

2

4

6

8

10

12

14

0 1 2 3 4 5

stdev. indoor operative temperature (oC)

ran

g_

top

(K

)

rang_top = 3.79 + 0.57 * stdev_ topR2 = 0.06, p =0.0580


0

2

4

6

8

10

12

14

0 1 2 3 4 5stdev. indoor operative temperature (oC)

ran

g_

top

(K

)

rang_top = 4.16 + 1.65 * stdev_ top

R2 = 0.26, p = 0.0034

All Buildings

0

2

4

6

8

10

12

14

0 1 2 3 4 5

stdev. mean indoor operative temperature (oC)

ran

g_

top

(K

)

rang_top = 3.19 + 1.82 * stdev. topR2 = 0.35, p = 0.0001

Figure 3.9: Dependence of the acceptable range of operative temperatures (TOP) within buildings on their standard deviation of operative temperature indoors

The adaptive hypothesis emphasises the effects of expectation on thermal acceptability. If a

particular building’s indoor climate is characterized by large variations in temperature, both

temporally and spatially, the adaptive hypothesis predicts a corresponding widening in the

range of indoor temperatures considered acceptable by its occupants. Figure 3.9 depicts

the linear relationship between the range of acceptable operative temperatures and the

standard deviation of indoor operative temperature. The model was statistically significant

with a correlation coefficient r = +0.59, and the regression equation indicates that the

acceptable range (-0.85< ASH < +0.85) increases by about two degrees for a single degree

increase in standard deviation of operative temperature. So, in tightly controlled HVAC



buildings depicted in Figure 3.9 where we find relatively small standard deviations of

operative temperature, there is a clear trend for the gradient of ASH v TOP regression

models to increase (see Appendix A). Consequently the range of acceptable temperatures

appears to be much greater in naturally ventilated buildings where thermal variability is the

norm compared to HVAC buildings.

3.1.4. Thermal preferences and indoor climate

One hundred and sixteen of the 160 buildings in ASHRAE RP-884’s database assessed

thermal preferences with a questionnaire item along these lines:

“At this point in time, would you prefer to feel warmer, cooler, or no change?”

Probit regression analysis (Finney, 1971; Ballantyne, 1977) rather than linear regression has

been separately applied to the votes for warmer and cooler conditions for each building.

Preferred temperature (of whatever index) was defined as that value of the independent

variable (thermal index) corresponding to the intersection of the “want cooler” and “want

warmer” probit models (see Appendix B). Table 3.10 below summarises the main statistics

for preferred operative temperatures for the 116 buildings in which the questionnaire item

was available.

Table 3.10: Summary of the preferred operative temperatures (preftemp) (°C).







1 (1 missing value)

mean preftemp (±stdev) in the summer sample

23.1 (±1.26)

24.3 (±2.13)

24 (±0)




1 (1 missing value)

mean preftemp (±stdev) in the winter sample

22.9 (±1.19)

23.1 (±1.61)

21.7 (±0)

* results not based on statistically significant regression models.



The results in Table 3.10 indicate a fairly constant temperature preference of about 23°C in

centrally controlled HVAC buildings. Winter temperature preferences in naturally ventilated

buildings were on average about a degree cooler than summer preferences, but this failed

to meet statistical significance, due to the small sizes and large standard deviations. The

summer temperature preferences in HVAC buildings were on average about one degree

cooler than those in naturally ventilated buildings and because of the more substantial

sample sizes in this season, the difference was significant (T = 3.23, df = 84, p < 0.002).

The trivial difference in temperature preferences between HVAC and NV buildings in winter

at less than a third of a degree was statistically insignificant (T = 0.34, df = 26, p > 0.5).

All Buildings

17

19

21

23

25

27

29

17 19 21 23 25 27 29 31 33 35


pref

erre

d op

erat

ive

tem

pera

ture

(oC

)

preftemp = 16.21 + 0.30 * et

R2 = 0.30, p 0.0001

All Buildings

17

19

21

23

25

27

29

18 20 22 24 26 28 30 32 34


pref

erre

d op

erat

ive

tem

pera

ture

(oC

)

preftemp = 16.30 + 0.29 * top

R2 = 0.28, p = 0.0001

All Buildings

17

19

21

23

25

27

29

-1 -0.5 0 0.5 1 1.5 2 2.5

Predicted Mean Vote

pref

erre

d op

erat

ive

tem

pera

ture

(o C

)

preftemp = 23.15 + 1.30 * pmvR2 = 0.30, p = 0.0001

All Buildings

17

19

21

23

25

27

29

22 24 26 28 30 32 34

mean indoor standard effective temperature ( oC)

pref

erre

d op

erat

ive

tem

pera

ture

(oC

)

preftemp = 12.93 + 0.41* set

R2 = 0.29, p 0.0001

Figure 3.10 Thermal preferences as a function of mean indoor thermal index values (TOP, ET, PMV, SET). Each data point represents a single building.

Figure 3.10 indicates that the operative temperature preferred by building occupants was

moderately correlated with mean levels of warmth prevailing within their buildings at the time

of the field survey. The strength of correlation was reasonably consistent at about r=+0.55

across all four indoor climatic indices (TOP, ET, PMV and SET).



3.1.5. Comparisons between neutral and preferred temperatures indoors.

With various aspects of perceived indoor climates being assessed with different

questionnaire items, there is a possibility that the indoor temperatures defined as optimal

for a particular building and climatic context may in fact vary, depending on whether one is

talking in terms of thermal sensation (neutrality), thermal acceptability (satisfaction) or

thermal preference (preferred temperatures). Indeed, some authors (McIntyre, 1978; de

Dear, 1991c) have suggested that at least some of the statistical dependence of neutrality

on prevailing outdoor climates observed by the pioneers of adaptive models (Auliciems and

Humphreys) may in fact be due to a semantic artefact in the ASHRAE (or Bedford) 7-pt

scale of thermal sensation. Persons living in cold climates may in fact describe their

preferred thermal environment with words like “warm and cosy” while for persons in hot

climates, words like “cool and fresh” may connote their thermal ideal.

The RP-884 database contains 55 buildings in which both thermal sensations (ASH) and

thermal preferences were registered, and so each of these buildings had both a neutrality

and a preferred temperature available in the meta-analysis. A new variable called “semantic

discrepancy” (discrep) was calculated as neutrality minus preferred temperature and

expressed in degrees (°C).

Table 3.11: Statistics for the semantic discrepancy (discrep) between observed neutrality (neut_top) and observed temperature preference (preftemp) (°C).





43 out of 62

(17 missing values)

23 out of 24

(9 missing values)

1 out of 1

(1 missing value)

mean discrep (±stdev) in the summer sample

0.7

(±0.78)

0.2

(±1.38)

-0.14 (±0)


13 out of 22

(10 missing values)

6 out of 6

(no missing values)

1 out of 1

(1 missing values)

mean discrep (±stdev) in the winter sample

0.0

(±0.45)

0.3

(±1.00)

-0.7 (±0)

* only results from buildings with statistically significant regression models (neut_top) and probit analyses (preftemp) were used to define discrep



Table 3.11 indicates that in both seasons and in HVAC and NV buildings alike, the average

semantic discrepancy between neutrality and preference was greater than or equal to zero

degrees. Although the seasonal difference between mean DISCREP in NV buildings was

negligible (T = 0.15, df = 27, p > 0.5), the seasonal difference in HVAC buildings was

statistically significant (T = 2.93, df = 54, p < 0.01). Neither the summer nor the winter

differences between DISCREP in HVAC and NV buildings were significant (T = 1.96, df =

64, p > 0.05 and T = 0.73, df = 17, p > 0.2 respectively).

Figure 3.11 below was designed to test the hypothesis that warm environments promote

positive semantic discrepancies between thermal sensations and preferences, while cool

environments promote negative discrepancies. The seemingly random distribution of data

points in the graph and statistically insignificant correlation and regression in the “all

buildings” panel of Figure 3.11 suggest that mean indoor climatic warmth (top) appears to

exert no systematic influence on the semantics of thermal sensation scales.

Pursuing the semantic artefact hypothesis a little further, the database was disaggregated

into HVAC and naturally ventilated buildings. The lower panels of Figure 3.11 indicates

again that, for the naturally ventilated buildings at least, the mean levels of warmth indoors

had no systematic effect on DISCREP. However, there was a positive, albeit modest,

relationship between the DISCREP variable and mean indoor operative temperature in

HVAC buildings. The gradient on that regression model indicates that, on average,

neutrality inside a centrally air-conditioned building becomes elevated above preferred

temperature by about one degree for every two degrees the mean indoor operative

temperature increases between 21 and 26°C. That is, persons living and/or working in

generally warm centrally air-conditioned buildings seem to be describing their preferred

indoor climate with terms like “slightly cool” while persons in generally cool centrally air-

conditioned buildings seem inclined to describe their preferred indoor climate as “slightly

warm.”



All Buildings

-4

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32 34


dis

cre

p (o

C)

discrep = 0.12 + 0.01 * top

R2 = 0.002, p = 0.7034


-4

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32 34


dis

cre

p (

o C)

discrep = -12.12 + 0.54 * top

R2 0.25, p = 0.0001


-4

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32 34


dis

cre

p (

oC

)

discrep = 0.08 + 0.01 * top

R2 = 0.001, p = 0.8450

Figure 3.11: Dependence of discrep on mean indoor operative temperatures

3.1.6. Behavioural adjustments to indoor climate

As noted in the introductory chapter to this monograph, behavioral thermoregulation involves

a variety of purposive actions that modify the heat and mass exchanges that define the

body’s heat balance with its thermal environment. The most obvious behavioural response

for which we have quantitative data in the RP-884 database is that of clothing insulation.

The other “personal” or behavioral parameter governing the human body’s heat balance for

which we have quantitative estimates in the RP-884 database is metabolic heat. Thirdly,

indoor air speeds which were measured throughout the RP-884 database, is another

parameter over which building occupants exert some behavioral control, either by



opening/closing windows, or turning on/off fans and similar devices. The following sections

examine these data and their relationships with various indices of indoor climate.

3.1.6.1. Thermal insulation adjustments indoors

Clothing insulation and also the incremental insulation of the chairs upon which the subjects

were sitting at the time of their questionnaire response were converted into clo units

according to the ASHRAE Standard 55 1992 methods. Table 3.12 summarises the main

statistics.

Table 3.12: Statistics for the thermal insulation variable (clothes + furniture) (clo).





79

33

2

mean INSUL (±stdev) in the summer sample

0.70

(± 0.077)

0.66

(± 0.125)

0.71

(± 0.008) number of buildings in winter sample*

32

12

2

mean INSUL (±stdev) in the winter sample

0.92 (± 0.126)

0.93 (± 0.331)

0.83 (± 0.259)

Table 3.12 indicates significant seasonal differences in thermal insulation, with average

winter values exceeding 0.9 clo and average summer values around 0.7 clo (T=11.2,

df=109, p<0.001 for HVAC buildings; T=4.0, df=43, p<0.001 for NV buildings). While

seasonal differences were significant, the trivial differences between HVAC and NV sample

means failed to reach statistical significance in either season. However, building mean

insulation values showed greater variability in the naturally ventilated building sample

compared to the HVAC sample.

Figure 3.12 indicates a statistically significant relationship between the mean level of

thermal insulation worn inside a building and its mean indoor temperature. The

scattergrams in Figure 3.12 suggest that an exponential decay model might fit better than

the straight line printed in the graphs. However, due to the particular weighting




0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

10 15 20 25 30 35mean indoor operative temperature (oC)

cha

ir +

clo

thin

g (

clo

) insul = 1.73 - 0.04 * top

R2 = 0.18, p = 0.0001


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

10 15 20 25 30 35


clo

thin

g +

cha

ir (

clo

)

insul = 2.08 - 0.05 * top

R2 = 0.66, p = 0.0001

All Buildings

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

10 15 20 25 30 35


clo

thin

g +

ch

air

(cl

o)

insul = 1.87 - 0.04 * top

R2 = 0.51, p = 0.0001

Figure 3.12: Thermal insulation inside buildings (mean ± stdev) as a function of mean indoor operative temperatures

factors (i.e. sample sizes) applying to each point (building) in the graphs, the R2 statistic was

greatest for the simple linear fits shown in Figure 3.12. The correlation can be described as

“moderate” in the case of the “all buildings” graph of Figure 3.12. The lower panels indicate

that a much stronger relationship in the naturally ventilated buildings. This finding is possibly

due to the greater range of temperatures (independent variable) encountered in the naturally

ventilated building sample compared to the central HVAC/mixed mode building sample.



The error bars either side of the plotted points in Figure 3.12 represent ± one standard

deviation around the within-building mean. All three panels of Figure 3.12 indicate a general

tendency for the standard deviation bars to contract in towards the mean, i.e. the variability

of clothing insulation to decrease, as indoor temperature increased. This possibly reflects a

diminution of degrees of freedom to adjust clothing as the number of individual garments

being worn reduced towards the socially acceptable minimum dress standards.


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

16 18 20 22 24 26 28 30neutral indoor operative temperature (oC)

clo

thin

g +

ch

air

(cl

o) insul = 1.98 - 0.05 * neut_top

R2 = 0.33, p = 0.0001


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8


clo

thin

g +

ch

air

(cl

o)

insul = 1.66 - 0.04 * neut_topR2 = 0.10, p = 0.0607

All Buildings

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8


clo

thin

g +

ch

air

(cl

o)

insul = 1.66 - 0.04 * neut_topR2 = 0.13, p = 0.0002

Figure 3.13: Thermal insulation inside buildings (mean ± stdev) as a function of neutral operative temperature.



Figure 3.13 plots mean thermal insulation inside each building in relation to neutral operative

temperature. While the linear regression model is significant for the “all buildings” panel, the

lower panels of Figure 3.13 suggest that this is primarily attributable to the central HVAC

and mixed-mode buildings.

3.1.6.2. Metabolic rate adjustments indoors

Figure 3.14 depicts mean metabolic heat production estimates within each building in

relation to the mean indoor temperatures prevailing within the building. The error bars

represent ± one standard deviation. Apart from Brown’s eight industrial buildings which had

mean metabolic rates in the 2~3 met unit range, seven of which appear as outliers, the

remaining buildings in the RP-884 database had mean metabolic rates tightly clustered in

the 1.1~1.4 met unit range. Figure 3.14 indicates no discernible relationship between mean

metabolic rates, their within-building standard deviations, or mean temperatures within

buildings.

Figure 3.15 repeats the analysis of mean metabolic rates, this time in relation to the

neutrality observed inside each of the RP-884 database buildings. It seems reasonably

clear that there was no systematic relationship between metabolic rates and the

temperatures which building occupants described as “neutral.”




0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 15 20 25 30 35mean indoor operative temperature (

oC)

me

an

me

tab

olic

ra

te (

me

t) met = 1.09 + 0.003 * topR2 = 0.05, p = 0.1656


0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5


oC)

me

an

me

tab

olic

ra

te (

me

t) met = 1.53 - 0.01 * top

R2 = 0.01, p = 0.4419

All Buildings

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

10 15 20 25 30 35mean indoor operative temperature (oC)

me

an

me

tab

olic

ra

te (

me

t)

met = 1.53 - 0.01 * top

R2 = 0.01, p = 0.4419

Figure 3.14: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to mean operative temperature indoors.




0.0

0.5

1.0

1.5

2.0

2.5

3.0

16 18 20 22 24 26 28 30neutral indoor operative temperature (

oC)

me

an

me

tab

olic

ra

te (

me

t) met = 1.29 - 0.005 * neut_topR2 = 0.03, p = 0.3454


0.0

0.5

1.0

1.5

2.0

2.5

3.0


oC)

me

an

me

tab

olic

ra

te (

me

t) met = 1.32 - 0.01 * neut_top

R2 = 0.003, p = 0.6707

All Buildings

0.0

0.5

1.0

1.5

2.0

2.5

3.0

16 18 20 22 24 26 28 30

neutral indoor operative temperature (oC)

me

an

me

tab

olic

ra

te (

me

t)met = 1.32 - 0.01 * neut_top

R2 = 0.02, p = 0.2134

Figure 3.15: Mean metabolic rates (mean ± stdev) within buildings plotted in relation to neutral operative temperature

3.1.6.3. Air speed adjustments indoors

Table 3.13 presents the means and standard deviations of the within-building mean air

speeds. In all three types of building - HVAC, NV, and mixed-mode, there was a decrement

in mean indoor speeds from summer to winter. The seasonal difference reached statistical

significance in the case of HVAC and NV buildings (T=3.98, df=100, p<0.001 for HVAC;

T=3.62, df=41, p<0.001 for NV). The mean HVAC building air speeds in both summer and

winter samples fell below the draft limit of 0.2 m s-1 specified in ASHRAE Standard 55-92 for

situations in which the occupant has no control. The summer sample in Table 3.13 indicates

mean air speeds within the naturally ventilated buildings were twice as fast as in the HVAC

sample -- this difference was statistically significant (T=7.8, df=103, p<0.001).



Table 3.13: Statistics for mean indoor air speeds (m s-1).





74

5 missing values

31

2 missing values

1

1 missing value

mean VELAV (±stdev) in the summer sample

0.11 (± 0.037)

0.23 (± 0.120)

0.16 (± 0)


28

4 missing values

12

no missing values

2

no missing values

mean VELAV (±stdev) in the winter sample

0.08 (± 0.024)

0.10 (± 0.047)

0.12 (± 0.033)

Figure 3.16 plots the within-building means and standard deviations for indoor air speeds in

relation to mean temperatures indoors. The top panel indicates that almost half of the

between-building variance in the mean indoor air speeds could be accounted for by

variations in mean temperatures. The relationship was best approximated by a model that

expressed indoor air speed as an exponential function of indoor temperature. The lower

panels in Figure 3.16 indicate that this statistically significant relationship extended to both

central HVAC and naturally ventilated buildings, although the relationship was a simple linear

one in the case of HVAC buildings. This was probably because the range of mean

temperatures and mean air speeds observed in the HVAC building sample was relatively

restricted. But even across this restricted range of temperatures, the variability of indoor air

speeds, as indicated by the error bars (mean ±stdev) in Figure 3.16, tended to increase as

mean indoor temperatures increased. A stronger dependence of mean air speeds on mean

temperatures was observed in the sample of naturally ventilated buildings (lower right-hand

panel of Figure 3.16). The relationship for these buildings was clearly exponential and the

model was capable of explaining 53% of the between-building variance in mean air speeds.




0.0

0.2

0.4

0.6

0.8

1.0

10 15 20 25 30 35mean indoor operative tempetature (

oC)

me

an

ind

oo

r ve

loci

ty (

m/s

) vel = -0.56 + 0.03 * top

R2 = 0.34, p = 0.0001


vel = 0.0077e 0.1174 * top

R2 = 0.5312, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0


oC)

me

an

ind

oo

r ve

loci

ty (

m/s

)

All Buildings

vel = 0.0048e0.1314 * top

R2 = 0.4664, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0


oC)

me

an

ind

oo

r ve

loci

ty (

m/s

)

Figure 3.16: Indoor air speeds (building mean ± stdev) plotted in relation to mean operative indoor temperature.

Figure 3.17 examines the relationship between mean indoor air speed and the temperature

judged as “neutral” by each building’s occupants. As with the regressions on mean

temperature, the air speed observations were best approximated by a simple linear model

in the HVAC building sample. But the relationship across the extended range of dependent

and independent variables in naturally ventilated buildings was best approximated with an

exponential model, accounting for 35% of the between-building variance in mean speeds




0.0

0.2

0.4

0.6

0.8

1.0


oC)

me

an

ind

oo

r ve

loci

ty (

m/s

) vel = -0.16 + 0.01 * neut_top

R2 = 0.08, p = 0.0288


vel = 0.0068e 0.1358 * neut_top

R2 = 0.354, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

16 18 20 22 24 26 28 30


me

an

ind

oo

r ve

loci

ty (

m/s

)

All Buildings

vel = 0.005e0.137 * neut_top

R2 = 0.2407, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

16 18 20 22 24 26 28 30


me

an

ind

oo

r ve

loci

ty (

m/s

)

Figure 3.17: Indoor air speeds (building mean ± stdev) plotted in relation to neutral operative temperature.

3.2. Interactions with outdoor weather and climate

Much of what has been published to date on the subject of adaptive models of comfort has

emphasised the role of external climatic environment in forcing behavioral adjustments,

physiological acclimatization and thermal expectations. This section presents comfort data

from the RP-884 database in relation to the weather and climatic conditions prevailing

outside the study buildings.

3.2.1. Thermal neutrality and outdoor climate

Outdoor climate can be represented in these meta-analyses at two levels of detail:

a) simple seasonal comparisons (summer v winter), and



b) mean outdoor average ET* (dayav_et) during the period of each building’s survey

3.2.1.1. Seasonal comparisons

The raw data files obtained from original field researchers were classified by the RP-884

team into summer or winter, depending on month and location. Tropical locations were all

regarded as summer regardless of month.

Table 3.14: Seasonal comparisons of thermal neutralities* defined in terms of the four major indoor thermal indices (TOP, ET, PMV and SET)



t-test of the difference between the summer and winter samples for neut_top

t = 4.50 df = 59

p < 0.001

t = 2.09 df = 32

p < 0.05 t-test of the difference between the summer and winter samples for neut_et

t = 3.76 df = 59

p < 0.001

t = 1.47 df = 32 p > 0.1

t-test of the difference between the summer and winter samples for neut_pmv

t = 4.14 df = 28

p < 0.001

t = 1.08 df = 25 p > 0.2

t-test of the difference between the summer and winter samples for neut_set

t = 0.8 df = 30 p > 0.2

t = 3.47 df = 25

p < 0.002 * only results from buildings with statistically significant regression models in

Appendix A were used in this table

Thermal neutralities defined in terms of the simpler indices of operative temperature and

new effective temperature were significantly differentiated between seasons in centrally

heated or air-conditioned buildings, with the average neutrality in summer being about one

and a half degrees (K) warmer than in winter. However, their seasonal differences for

neutralities on the more sophisticated thermal indices of PMV and SET reached statistical

significance in Table 3.14 only for the PMV index.

The smaller sample size of naturally ventilated buildings in the RP-884 meant that the

seasonal comparisons in neutralities were less clear cut than they were for HVAC buildings

-- e.g. only two of the naturally ventilated buildings in winter achieved significant SET



regression models. As a result, only neutral operative temperatures and neutral Standard

Effective Temperatures were significantly differentiated between the summer and winter

seasons in Table 3.14. While the seasonal difference in neutral operative temperature was

over two degrees (K) and in the direction one might expect from the adaptive model’s

perspective, it should be pointed out that SET index neutrality difference between seasons

had a counterintuitive sign -- the winter neutrality was warmer than the summer one.

Not listed in Table 3.14 due to small sample sizes, but worth mentioning is the seasonal

difference in neutrality for buildings classified as “mixed mode”. Only one such building

managed a significant regression model with operative temperature in summer, but its

neutrality in that season was 23.9°C compared to the average winter neutrality of 20.7°C

recorded in such buildings.

3.2.1.2. Dependence of observed neutrality on outdoor climate

Linear regression models were constructed for the relationship between indoor thermal

neutrality and mean outdoor warmth. The former was assessed in terms of the operative

temperature index (neut_top) while outdoor warmth was parameterized in terms of mean

daily effective temperature (dayavet). Figure 3.18 shows the resulting regression models,

and the “all buildings” panel indicates a reasonably strong correlation for this relationship,

with r = +0.65. The regression coefficient in that model suggests that operative temperature

neutrality indoors changes by one degree (K) for about six degrees change in mean daily

outdoor effective temperature.



all buildings from the RP-884 database

15

17

19

21

23

25

27

29

-5 0 5 10 15 20 25 30 35mean outdoor effective temperature (oC)

neut

ral i

ndoo

r ope

rativ

e te

mpe

ratu

re (

o C)

neutrality = 20.9 + 0.16 (outdoor ET*)

R2 = 0.42, p = 0.0001

Central HVAC and Mixed Mode buildings, from the RP-884 database

15

17

19

21

23

25

27

29

-5 0 5 10 15 20 25 30 35

mean outdoor effective temperature (oC)

ne

utr

al i

nd

oo

r o

pe

rati

ve

tem

pe

ratu

re (o C

)

neut_top = 21.5 + 0.11 * dayavet

R2 = 0.53, p = 0.0001

naturally ventilated Buildings from the RP-884 database

15

17

19

21

23

25

27

29

-5 0 5 10 15 20 25 30 35


ne

utr

al i

nd

oo

r o

pe

rati

ve

tem

pe

ratu

re (o C

)

neut_top = 18.9 + 0.255 * dayavet

R2 = 0.42, p = 0.0001

Figure 3.18: Dependence of indoor neutrality on outdoor climate

The slope of the model for naturally ventilated buildings in Figure 3.18 indicates that indoor

thermal neutrality increased by approximately one degree (K) for every four degree (K)

increase in mean daily outdoor effective temperature (dayavet). The gradient for centralized

HVAC buildings was less than half the naturally ventilated buildings’ result. Using the T-test

method for comparing two straight lines using separate regression fits, as described in

Kleinbaum et al. (1988), we obtained a T statistic of 3.25 (d.f.=101), which was statistically

significant (p < 0.01).



3.2.1.3. Analysis of predicted neutralities with respect to mean outdoor temperature

The PMV model was used to predict thermal neutralities for each of the buildings in the RP-

884 database simply by using building mean values for each of the heat balance model’s

input parameters, and then iterating the operative temperature input until PMV=0. The

predicted neutralities, codenamed predneut, have been plotted in Figure 3.19 in relation to

mean daily average outdoor effective temperatures. With the exception of Brown’s seven

HVAC light industrial buildings, predicted neutralities demonstrate a moderate linear

dependence on outdoor climate. The anomalous industrial buildings probably result from the

significantly higher metabolic rates of their occupants. Metabolic rate is one of the six input

parameters to the heat balance model which was used to predict these neutralities. Figure

3.20 shows the regression model for HVAC buildings with these outliers excluded from the

analysis. While the regression coefficient changed little after these exclusions, the amount of

variance in predneut accounted for by the model increased to 25%



All Buildings

10

15

20

25

30

-30 -20 -10 0 10 20 30 40


pred

icte

d ne

utra

lity

(oC

)

predneut = 21.6 + 0.08 * dayavet

R 2 = 0.14, p = 0.0001


10

15

20

25

30

-30 -20 -10 0 10 20 30 40


pred

icte

d ne

utra

lity

(oC

)


R2 = 0.10, p < 0.001


10

15

20

25

30

-30 -20 -10 0 10 20 30 40


pred

icte

d ne

utra

lity

(o C)


R 2 = 0.30, p < 0.001

Figure 3.19: Dependence of PMV-based neutrality predictions on outdoor climate




10

15

20

25

30

-30 -20 -10 0 10 20 30 40


pred

icte

d ne

utra

lity

(oC

)

predneut = 22.6 + 0.04 * dayavetR

2 = 0.25, p <0.001

Figure 3.20: Dependence of PMV-based neutrality predictions on outdoor climate for HVAC buildings with sedentary occupants

The statistical significance of regression models for PMV-predicted neutrality, as plotted in

Figures 3.19 and 3.20, probably reflects the dependence of some of the basic heat-balance

variables such as clothing insulation and indoor air speed on outdoor climate. These

mediating variables will be subjected to further detailed analysis in relation to outdoor

climate in a subsequent section dealing with behavioral responses (Section 3.2.4).

The predicted neutralities in naturally ventilated buildings (Figure 3.19) were almost twice as

sensitive to outdoor temperature than was the case for HVAC buildings (Figure 3.19), and

this was confirmed with Kleinbaum’s (1988) statistical test for the difference between

independent regression coefficients (T=3.64, df=148, p<0.01).

3.2.2. Thermal acceptability and outdoor climate

Section 3.1.3 indicated that thermal satisfaction ratings within buildings bore little

relationship with mean indoor climatic indices (Figure 3.6). This section examines whether

or not thermal acceptability is related to outdoor climate in any way. Figure 3.21 presents

polynomial regression models separately for a) directly assessed thermal acceptability (tsa),

and b) thermal acceptability inferred from thermal sensation votes (prxy_tsa). The y-axis



variables in both graphs represent the number of occupants within each building expressing

thermal acceptability as a percentage of the whole building sample. Both direct and inferred

versions of building thermal acceptability ratings failed to show any signs of a statistically

significant relationship with outdoor climate in Figure 3.21.

Directly assessed thermal acceptability, all buildings

0

20

40

60

80

100

-30 -20 -10 0 10 20 30 40


TSA

(% a

ccep

tabl

e)

tsa = 72.78 - 0.30 * dayavet + 0.02 * dayavet2

R2 = 0.06, p = 0.1683

Thermal acceptability inferred from thermal sensation votes, all buildings

0

20

40

60

80

100

-30 -20 -10 0 10 20 30 40mean outdoor effective temperature (oC)

Pro

xy T

SA

(%

acce

ptab

le)

prxy_tsa = 79.66 - 0.25 * dayavet + 0.01 * dayavet 2

R2 = 0.003, p = 0.7682

Figure 3.21: Thermal acceptability and outdoor climate. TSA represents directly assessed thermal acceptability levels within each building and PRXY_TSA represents thermal acceptability inferred from thermal sensation votes.

A building’s thermal acceptability rating, as inferred from thermal sensation votes, is logically

related to the gradient of that building’s thermal sensation regression models with respect to

indoor temperature (Appendix A). The range of acceptable operative temperatures for each

building was defined as rang_top in the RP-884 meta-analysis and presented earlier in

Section 3.1.3.4 simply by solving the mean thermal sensation versus mean indoor operative

temperature regression model (Appendix A) for mean ASHRAE thermal sensation votes of

±0.85. These values were chosen on the basis of Fanger’s PMV/PPD model (Fanger,

1970) which suggests they correspond to 80% acceptability levels (PPD=20%).

The complete lack of any statistical relationship between the range of acceptable indoor

operative temperatures (rang_top) and mean daily outdoor effective temperature (dayavet)

is evident in Figure 3.22.




0

2

4

6

8

10

12

14

-5 0 5 10 15 20 25 30 35


rang

_top

(K)

rang_top = 4.07 + 0.02 * dayavet

R2 = 0.01, p = 0.8460


0

2

4

6

8

10

12

14

-5 0 5 10 15 20 25 30 35mean outdoor effective temperature (oC)

rang

_top

(K)

rang_top = 8.63 - 0.04 * dayavetR2 = 0.01, p = 0.6752

Figure 3.22: The range of acceptable operative temperatures indoors plotted in relation to the mean outdoor effective temperature.

3.2.3. Thermal preference and outdoor climate

Section 2.8.5 described how preferred temperature was derived for each building with the

MCI questionnaire item by locating the intersection of the “want cooler” probit model with the

“want warmer” model (see Appendix B). The indoor operative temperature corresponding

with the intersection of “cooler” and “warmer” probit models was incorporated into the RP-

884 meta-analysis as preftemp. Conceptually, preftemp is directly analogous to the

neutrality (neut_top), only it was derived from thermal preference votes instead of thermal

sensations. Regression models of the dependence of preftemp on mean outdoor effective

temperature (dayavet) are presented in Figure 3.23.



All Buildings

18

20

22

24

26

28

30

-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35


pref

erre

d te

mpe

ratu

re (

oC

)

preftemp = 22.49 - 0.01 * dayavet + 0.003 * dayavet2

R2 = 0.13, p = 0.0004


18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35mean outdoor effective temperature (

oC)

pref

erre

d te

mpe

ratu

re (

oC

) preftemp = 22.82 - 0.001*dayavet + 0.001 * dayavet2

R2 = 0.05, p = 0.3871


preftemp = 19.3 + 0.22 dayavet

R2 = 0.43 p<0.00001

18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35mean outdoor effective temperature (

oC)

pref

erre

d te

mpe

ratu

re (

oC

)

Figure 3.23: Dependence of indoor preferred temperatures on outdoor climate

The regression models show a relatively weak relationship between indoor temperature

preferences and outdoor climate in the “all buildings” panel of Figure 3.23. A slightly

parabolic trend is discernible in the plot, but there is insufficient data from climates with sub-

zero mean daily outdoor effective temperatures to be confident about this trend. The

temperature preference data become clearer when they are plotted separately for the HVAC

and NV building sub-samples in Figure 3.23. Basically there is no discernible relationship in

the case of HVAC and mixed-mode buildings, with the second order polynomial model

failing to reach statistical significance at the 0.05 level. However, the naturally ventilated

sub-sample of preferred temperatures, albeit small (n=30), demonstrates a clear



relationship with outdoor climate, with the linear regression model explaining 43% of the

variance (r= +0.66). The NV subsample’s scatter plot in Figure 3.23 suggest a slightly

curvilinear relationship, and although the addition of a second-order polynomial term in the

regression model lifted the R2 statistic to 46%, neither the first-order nor second-order

regression coefficients reached statistical significance (T=0.55, p=0.54; T=1.12, p=0.25 for

X1 and X2 terms respectively), so the simple linear regression model has been retained for

Figure 3.23.

Since preferred temperatures represent an alternative definition to thermal neutrality for

optimal indoor temperature, it becomes interesting to compare them. Traditionally, thermal

comfort researchers have regarded thermal neutrality and preference as synonymous, but

some published evidence suggests that there may in fact be a semantic discrepancy in the

way the two scales are actually interpreted by building occupants (McIntyre, 1978; de Dear

et al, 1991c). This semantic artefact hypothesis predicts that thermal neutralities will shift to

warmer temperatures than actually preferred when the climatic context is warm, while the

offset will be cooler-than-preferred in cold climatic contexts. Figure 3.24 tests this

hypothesis by plotting the discrepancy between neutrality and preference (discrep) in

relation to mean outdoor effective temperature. The main panel (“all buildings”) depicts a

statistically significant weighted regression model with the slope as predicted by the

semantic artefact hypothesis. However, the linear fit is very poor, with the model’s gradient

indicating that thermal neutrality drifts apart from preference at the rate of only one degree

(K) for every 25 K shift in mean outdoor effective temperature. The model fitted to the small

sample of naturally ventilated buildings failed to achieve any statistical significance, but the

same was not true for the HVAC and mixed-mode sample of buildings. The lower left panel

of Figure 3.24 indicates the linear model accounted for about 38% of the variance in discrep

and that thermal neutrality diverged from preference at the rate of about one degree (K) for

every 14 K change in outdoor temperature.



All Buildings

-4

-3

-2

-1

0

1

2

3

-5 0 5 10 15 20 25 30 35


dis

cre

p (

oC

)

discrep = -0.42 + 0.04 * dayavet

R 2 = 0.06, p = 0.0195


-4

-3

-2

-1

0

1

2

3

-5 0 5 10 15 20 25 30 35


dis

cre

p (

oC

)

discrep =-0.95 + 0.07 * dayavet

R 2 = 0.38, p = 0.0001


-4

-3

-2

-1

0

1

2

3

-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (

oC)

dis

cre

p (

oC

)

discrep = 0.23 + 0.01 * dayavet

R2 = 0.001, p = 0.8501

Figure 3.24: The discrepancy (discrep) between thermal neutrality (neut_top) and preference (preftemp) plotted against mean outdoor climate (dayavet).

3.2.4. Behavioural responses to outdoor climate

Since all of the input variables to the heat balance model of thermal comfort are available

within the RP-884 database, it is possible to explore their variations and relationships with

respect to outdoor climate. This subsection focuses on the main adaptive adjustments

involved in the human body’s heat balance with indoor climate -- in particular, clothing

insulation, metabolic rate and indoor air speed.



3.2.4.1. Indoor clothing and outdoor climate

The amount of clothing insulation worn indoors was shown in Section 3.1.6.1. to be related to

indoor climatic indices such as mean indoor operative temperature. It seems reasonable to

expect that clothing decisions and behavior are also influenced by outdoor weather and

climatic differences. To examine this possibility Figure 3.25 plots mean thermal insulation

values for the occupants of each building (insul), comprised of both clothing and chair

components, against mean outdoor effective temperatures prevailing at the time of the

building samples survey. For the HVAC and naturally ventilated building samples combined,

40% of the variance in clo values was explained by variations in the outdoor climatic index.

An exponential decay curve fitted the data significantly better than a straight line model,

probably reflecting the effects of a minimum socially acceptable level of thermal insulation at

about 0.4 clo units (after subtracting 0.15 clo units for chair effects).

The error bars (standard deviations) around each building point plotted in Figure 3.25

suggest a trend towards increasing homogeneity in thermal insulation for those buildings

located in warmer climates. This presumably also reflects the fact that clothing decisions

and behavior have fewer degrees of freedom as the level of clothing approaches the

minimum socially acceptable threshold.

When the clothing database was disaggregated by building type (HVAC v NV), thermal

insulation was also found to decay exponentially with outdoor temperature in the HVAC

buildings where the regression model was found to account for about 64% of the variance in

insul. However, in the case of naturally ventilated buildings, a straight line regression model

produced the best fit to the data, with only 44% of variance being explained (r= -0.66). The

rate of insulation change with respect to outdoor temperature within the naturally ventilated

buildings was almost one tenth of a clo unit for every three degrees (K) of outdoor effective

temperature change, and this gradient appears in Figure 3.25 to be significantly steeper

than in the HVAC and mixed-mode buildings.




insul = 0.9343e-0.0127 * dayavet

R 2 = 0.64, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

-25 -15 -5 5 15 25 35


chai

r +

clot

hing

(cl

o)


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

-25 -15 -5 5 15 25 35


clot

hing

+ch

air

(clo

)

insul = 1.44 - 0.03 * dayavet

R 2 = 0.44, p = 0.0001

All Buildings

insul = 0.9346e-0.0133 * dayavet

R2 = 0.40, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35


clot

hing

+ch

air

(clo

)

Figure 3.25: Building occupants’ thermal insulation (clothing plus chair) as a function of outdoor temperature

3.2.4.2. Metabolic rate indoors related to outdoor climate

Figure 3.26 indicates a complete absence of any systematic relationship between mean

metabolic rates registered within buildings and the mean outdoor temperature prevailing at

the time of the survey. This generalization applies to both HVAC and naturally ventilated

buildings.




0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-25 -15 -5 5 15 25 35


mea

n m

etab

olic

rat

e (m

et)

met = 1.21 - 0.0005 * dayavet

R2 = 0.001, p = 0.7527


0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-25 -15 -5 5 15 25 35


mea

n m

etab

olic

rat

e (m

et)

met = 1.13 + 0.002 * dayavet

R2 = 0.03, p = 0.2851

All Buildings

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35

mean daily outdoor effective temperature (oC)

mea

n m

etab

olic

rat

e (m

et)

met = 1.18 - 0.000004 * dayavet

R 2 = 0.00, p = 0.9977

Figure 3.26: Metabolic rates of building occupants plotted in relation to mean outdoor climate

3.2.4.3. Indoor air speeds in relation to outdoor climate

Occupants of buildings, both with and without centralized HVAC, tend to increase indoor air

movement when or where temperatures increase. In the case of naturally ventilated

buildings in humid climates, this is typically achieved by opening windows and turning on

fans. In very hot and dry climates, windows are often kept shut, leaving just indoor fans to

accelerate air movement within the occupied zone. The same is often the case in buildings

with centralized HVAC services -- because windows are typically sealed, occupants resort

to using localised fans to supplement the typically low levels of air movement generated



within the occupied zone by conventional HVAC diffusers. All of this applies to situations of

elevated indoor temperature. The present section explores the relationship between indoor

air speeds and outdoor climate. Obviously indoor temperatures correlate with those

outdoors for naturally ventilated buildings, but this should not be the case in HVAC buildings

if the current (static) thermal comfort standards (ISO and ASHRAE) are being strictly

applied.


velav = 0.08e0.0135 * dayavet

R2 = 0.19, p<0.05

0.0

0.2

0.4

0.6

0.8

1.0

-25 -15 -5 5 15 25 35


mea

n in

door

vel

ocity

(m

/s)


velav = 0.03 e0.0758 * dayavet

R2 = 0.64, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

-25 -15 -5 5 15 25 35


mea

n in

door

vel

ocity

(m

/s)

All Buildings

velav = 0.07 e0.0258 * dayavet

R 2 = 0.25, p < 0.05

0.0

0.2

0.4

0.6

0.8

1.0

-25 -20 -15 -10 -5 0 5 10 15 20 25 30 35


mea

n in

door

vel

ocity

(m

/s)

Figure 3.27: Relationship between indoor air speeds and outdoor climate



Figure 3.27 plots mean building air speeds against the mean outdoor effective temperature

at the time of the building’s survey. The error bars around each data point represents ± one

standard deviation. The main panel of Figure 3.27 indicates a moderate correlation

between mean indoor air speeds and mean outdoor temperatures (r= +0.50). An

exponential function rather than straight line achieved the best fit to the data, reflecting the

effects of a minimum mean air speed of about 0.05 m s-1. The graph demonstrates clearly

that not only the mean speed within a building increases as outdoor temperature increases,

but also the variability in speeds around the mean increases.

These generalizations extend to the separate HVAC and NV samples’ analyses in Figure

3.27. The model fitted to the HVAC and mixed-mode sample indicates a modest increase

in air speeds from an average of below 0.10 m s-1 in cold climates to about 0.2 m s-1 in the

hot climates. Whether this increase is due to increased air speeds from HVAC diffusers or

local air movement generated by desk, ceiling or floor fans is unsure, but the relationship

accounted for 23% of variance in mean buildings air speeds (r=0.48). The strongest

correlation in Figure 3.27 was found in the naturally ventilated buildings where an exponential

regression model accounted for almost 64% of the variance in the dependent variable (r=

+0.80). Mean air speeds in cold-climate naturally ventilated buildings were similar to those

in HVAC buildings, below 0.10 m s-1, but increased to values in excess of 0.4 m s-1 in the

warmer climates represented in the RP-884 database.

3.3. Influence of building characteristics on thermal comfort

Comparisons between thermal comfort experiences of HVAC buildings and naturally

ventilated buildings have been made throughout the preceding sections of this chapter and

several significant differences have been discussed. The present section goes further into

the analyses of these contextual factors (as opposed to indoor or outdoor climatic features),

including the index of perceived control which we introduced in Section 2.4.

3.3.1. HVAC versus natural ventilation

The regression gradients depicted for each building in Appendix A suggest that thermal

sensations were about twice as sensitive to changes in indoor operative temperature in

centrally heated and air-conditioned buildings than in naturally ventilated buildings.



Unfortunately the sample size of mixed mode buildings was too small to draw any conclusive

comparisons, but the mean gradient fell, as might be expected of such buildings, about

midway between that for HVAC and naturally ventilated buildings. This heightened

sensitivity in HVAC buildings extended to the ET index as well, but did not persist when the

fully developed heat-balance indices such as PMV and SET were subjected to the

regression analyses, suggesting that factors such as clothing insulation and air speed were

responsible for any differences observed when using the simpler indoor climatic indices.

3.3.1.1. Thermal sensation and sensitivity in HVAC versus naturally ventilated buildings

Comparisons between thermal neutralities in centrally heated/air-conditioned building

sample with those in the naturally ventilated buildings have been summarised in Table 3.15.

Table 3.15: Comparisons of thermal neutrality observed in the HVAC and naturally ventilated building samples

Neutrality defined in terms of four indoor climatic indices

summer sample

winter sample

t-test of the difference between HVAC and NV buildings for neut_top

t = 1.16 df = 72 p > 0.1

t = 0.14 df = 19 p > 0.5

t-test of the difference between HVAC and NV buildings for neut_et

t = 1.12 df = 70 p > 0.2

t = 1.00 df = 21 p > 0.2

t-test of the difference between HVAC and NV buildings for neut_pmv

t = 1.48 df = 47 p > 0.1

not valid due to low

sample size t-test of the difference between HVAC and NV buildings for neut_set

t = 0.61 df = 47 p > 0.5

not valid due to low

sample size * only results from buildings with statistically significant regression models used in

these comparisons

None of the HVAC - NV comparisons in Table 3.15 reached statistical significance. That is,

in summer or in winter, there was no significant difference in neutrality between centrally

conditioned and naturally ventilated buildings, regardless of which thermal index was used to

define it.

As noted earlier in Table 3.1, Section 3.1.1.1, thermal sensitivity (i.e. the rate of change in

sensation votes, ASH, with respect to indoor temperature) was greater in the centrally



heated/cooled buildings than in naturally ventilated buildings within the database. This was

particularly so when indoor warmth was defined simply in terms of operative or effective

temperature indices. But again, when the fully-developed heat balance indices of indoor

warmth, namely PMV and SET, were substituted into the analysis, these differences

became less significant, suggesting that heightened thermal sensitivity in HVAC buildings

was at least partly the result of other heat balance factors such as clothing and air speed

remaining relatively static. Conversely, the relative thermal insensitivity of occupants of

naturally ventilated buildings appears to be largely the result of the ability to manipulate

physical variables affecting their body’s heat balance. Table 3.16 below summarises the

comparisons between classes of buildings in the database.

Table 3.16: Comparison of thermal sensitivity for centrally controlled buildings (HVAC) and naturally ventilated buildings (NV)

ASH v TOP ASH v ET ASH v PMV ASH v SET model mean gradient for HVAC buildings 0.51

0.50 0.74 0.21

model mean gradient for NV buildings 0.27 0.28 0.62 0.18

t-test of the difference between HVAC and NV model gradient means

t = 5.37 df = 97

p < 0.001

t = 4.45 df = 96

p < 0.001

t = 1.56 df = 56 p > 0.1

t = 1.00 df = 57 p > 0.2


The question left unanswered in this table is “why are occupants of naturally ventilated

buildings inclined to behaviorally regulate their heat balance to a greater extent than their

counterparts within HVAC buildings?” Is it the result of greater adaptive opportunities in

naturally ventilated buildings, particularly with respect to air speed, or is it the result of a

reluctance to thermoregulate with clothing adjustments on the part of HVAC building

occupants? Questions of adaptive opportunity and perceived control will be examined later

in Section 3.3.2.

3.3.1.2. Thermal acceptability in HVAC versus naturally ventilated buildings

Earlier sections have demonstrated little systematic relationship, if any, between directly

assessed thermal acceptability ratings and physical measurements of indoor climate. The



percentage of building occupants voting “acceptable” was, on average, 81.6% in centrally

air-conditioned buildings with a standard deviation of acceptability ratings at 10.1%. The

mean rating is just at the minimum acceptability threshold suggested in the thermal comfort

standards such as ASHRAE 55 and ISO 7730. The corresponding mean for naturally

ventilated buildings was 66.8%, and the between-buildings standard deviation of 20.1% was

twice that of HVAC sample. The difference of less than 15% acceptability, whilst statistically

significant (T=3.74; df=59, p<0.001), suggests that centralized air-conditioning enhances

perceived quality of internal environments only moderately. This interpretation, however,

ignores questions about where the two samples were drawn from -- were the naturally

ventilated buildings selected for the RP-884 database drawn from mild climate zones? Did

the HVAC building sample cover a much broader spectrum of climates, including some

which may have rendered passive architectural alternatives infeasible?

Since directly assessed thermal acceptability ratings were not universally available

throughout the database, attention turns to the indirect assessments derived from thermal

sensation votes. An earlier section of the current chapter gave the definition of “range of

acceptable temperatures” as those coinciding with mean thermal sensations of ±0.85 on the

linear regression models of Appendix A. This temperature range for each building,

codenamed rang_top in the meta-analysis, is inversely related to thermal sensitivity, as

noted in the preceding section. Therefore it is not surprising to find that the mean rang_top

in naturally ventilated buildings was about 70% wider than in centrally air-conditioned

buildings (see Table 3.9). The significance of the difference in acceptable temperature

ranges between HVAC and NV buildings was retained when the thermal index switched to

SET (i.e. the RANG_SET variable T=2.49, df=55, p<0.02). This implies that the extended

range of acceptability within naturally ventilated buildings could not be accounted for purely in

terms of physical heat-balance adjustments (clothing and air speed), and that other types of

adaptive response such as acclimatisation and shifting expectations may indeed influence

thermal acceptability.

3.3.1.3. Thermal preferences in HVAC versus naturally ventilated buildings.

Appendix B established the preferred operative temperatures for each building in which

some variant of the so-called “McIntyre scale” (MCI) was presented on the questionnaire.



The present section examines the dependence of preferred temperatures on indoor and

outdoor warmth, separately, for HVAC and naturally ventilated buildings during winter and

summer seasons. Figure 3.28 below presents the relationship between thermal preferences

and indoor temperature.

Central HVAC and Mixed Mode Buildings in Summer

18

20

22

24

26

28

30

18 20 22 24 26 28 30 32


pre

ferr

ed

te

mp

era

ture

(o

C)

preftemp = 30.38 - 0.31 * topR2 = 0.03, p = 0.1618

Naturally Ventilated Buildings in Summer

18

20

22

24

26

28

30

18 20 22 24 26 28 30 32


pre

ferr

ed

te

mp

era

ture

(oC

)preftemp = 13.28 + 0.39 * top

R2 = 0.34, p = 0.0029

Central HVAC and Mixed Mode Buildings in Winter

18

20

22

24

26

28

30

18 20 22 24 26 28 30 32


pre

ferr

ed

te

mp

era

ture

(o C)

preftemp = 16.22 + 0.29 * topR2 = 0.02, p = 0.5165

Naturally Ventilated Buildings in Winter

18

20

22

24

26

28

30

18 20 22 24 26 28 30 32


pre

ferr

ed

te

mp

era

ture

(o C) preftemp = 13.52 + 0.47 * top

R2 = 0.50, p = 0.1142

Figure 3.28: Dependence of thermal preferences on mean indoor warmth for HVAC and naturally ventilated buildings

The relatively tight temperature control within HVAC (and mixed-mode buildings) is reflected

as a restricted range on abscissa for Figure 3.28 and the statistically insignificant

regression models fitted across this narrow band of the independent variable suggest

thermal preferences were unrelated to mean temperatures inside HVAC buildings.

However, the naturally ventilated buildings in Figure 3.28 present a significantly different

picture, particularly those buildings sampled during summer, in which we found that the

preferred temperature increased by one degree (K) for every 2.5 K increase in mean indoor



temperature. The same trend was apparent in the winter sample as well, but due to the

limited number of NV buildings sampled in that season, the regression model failed to reach

statistical significance in the lower-right panel of Figure 3.28.

Central HVAC and Mixed Mode Buildings in Summer

18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35


pre

ferr

ed

te

mp

era

ture

(oC

)

preftemp = 23.19 - 0.01 * dayavet

R2 = 0.0004, p = 0.8738

Naturally Ventilated Buildings in Summer

18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35


pre

ferr

ed

te

mp

era

ture

(o C) preftemp = 17.29 + 0.29 * dayavet

R2 =0.41, p = 0.0008

Central HVAC and Mixed Mode Buildingsin Winter

18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35


pre

ferr

ed

te

mp

era

ture

(oC

)

preftemp = 23.09 - 0.05 * dayavetR2 = 0.11, p = 0.1139

Naturally Ventilated Buildings in Winter

18

20

22

24

26

28

30

-25 -15 -5 5 15 25 35


pre

ferr

ed

te

mp

era

ture

(o C) preftemp = 19.78 + 0.26 * dayavet

R2 = 0.47, p = 0.1352

Figure 3.29: Dependence of thermal preferences on mean outdoor warmth for HVAC and naturally ventilated buildings

Figure 3.29 examines temperature preferences within HVAC and naturally ventilated

buildings in relation to mean outdoor temperatures for summer and winter seasons. As was

the case for the indoor analyses just presented in Figure 3.28, the insignificant models

suggest that thermal preferences within HVAC buildings apparently have no systematic

relationship with the temperatures prevailing outdoors. This generalization does not extend

to naturally ventilated buildings, especially in the summer season where the RP-884

database contains a reasonable sample size. Figure 3.29 indicates that the operative



temperature preferred inside such buildings increased by about one degree (K) for every

three degrees (K) increase in mean daily outdoor effective temperature.

3.3.2. Personal environmental control

The literature review of the adaptive thermal comfort hypothesis in Chapter 1 indicated that

concepts of adaptive opportunity and perceived control play an important part in the

processes of thermal perception. Unfortunately only a handful of field research projects

within the RP-884 database explicitly included questionnaire items on these issues, and

those that did may not have used directly comparable versions of questionnaire. The

simple, albeit crude, solution proposed in the RP-884 database was to infer levels of

perceived control for each building in the sample from the following items of information:

• a knowledge of which adaptive opportunities were available within the building (operable

windows, doors, thermostats, fans, blinds etc), and

• how much each of these adaptive opportunities contributed to overall levels of perceived

control. This second step was quantified on the basis of a sub-sample of RP-884

buildings where the relevant questionnaire items were available for detailed analysis (see

Section 2.4).

A synthesis of these details led to an index of perceived control (PCC_AG) for about two

thirds of the buildings within the RP-884 database. This section of the RP-884 final report

presents some basic descriptive statistics for this index and some preliminary analyses of

its relationship with thermal perception (sensation, acceptability and preference).

Table 3.17: Summary of the perceived control index (pcc_ag).




number of buildings




mean (±stdev) mpcc_ag 1.5 (±0.73)

2.9 (±1.84)

6.2 (±0.24)

* based on those buildings in which adaptive opportunities were recorded.

As might be expected, mean levels of the perceived control index were lowest in those

buildings with centralized HVAC systems in place and highest in those buildings classified



as “mixed mode” where benefits of both natural ventilation and air-conditioning were

available for the occupants to use, as and when they saw fit. Naturally ventilated buildings

typically had middle-ranking values on the perceived control index. The difference in mean

pcc_ag between centralized HVAC and NV buildings was statistically significant (t = 5.52, df

= 104, p < 0.001).

Admittedly we are uncertain that the index of perceived control (PCC_AG) developed in this

study conforms to all the assumptions necessary for linear regression. Bearing this caveat

in mind, preliminary investigations of its relationships with thermal perceptual variables were

performed. Several of the variables from earlier sections of this chapter represented logical

candidates for these exploratory analysis.

All Buildings

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

0 1 2 3 4 5 6 7mean percieved control index (pcc_ag)

ther

mal

sen

sitiv

ity

(mea

n gr

ad_t

op)

grad_top = 0.36 - 0.02 * pcc_agR2 = 0.03, p = 0.1574

All Buildings

-0.3

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

1.7

0 1 2 3 4 5 6 7

mean percieved control index (pcc_ag)

ther

mal

sen

sitiv

ity

(mea

n gr

ad_s

et)

grad_set = 0.17 + 0.004 * pcc_ag

R 2 = 0.01, p = 0.5392

Figure 3.30: Regression analysis between thermal sensitivity and mean perceived control index (pcc_ag)

The adaptive model predicts that occupants of buildings in which there is a high level of

perceived control over thermal conditions will be less critical of indoor climatic conditions

than those in tightly regulated environments. Translating this hypothesis to the RP-884 meta-

analysis, Figure 3.30 plots each building’s thermal sensitivity statistic (dependence of

thermal sensation votes on either operative or standard effective temperature indices) in

relation to the building’s perceived control index score. The failure to reach statistical

significance in both the operative temperature and standard effective temperature index

graphs of Figure 3.30 lends no support to the adaptive hypothesis.



Another extension of this perceived control hypothesis predicts that buildings with high

degrees of occupant control would score higher ratings on thermal acceptability than those

with low levels of control. Figure 3.31 fails to support this hypothesis since there is a

complete absence of any relationship between buildings’ direct thermal acceptability ratings

and their perceived control index score.

All Buildings

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7

mean pcc_ag

TS

A (

% a

ccep

tabl

e)

tsa = 82.16 - 0.90 * pcc_ag

R2 = 0.03, p = 0.1904

Figure 3.31: Regression analysis between direct thermal acceptability rating of buildings (f_tsa_2) and their mean level of perceived control (pcc_ag)

Another corollary of the adaptive thermal control hypothesis is that occupants of buildings in

which there is high thermal controllability should be less likely to request a change of

temperature when presented with the thermal preference questionnaire item (MCI). Testing

this prediction with the RP-884 database can be done by tallying the percentage of each

building’s occupant sample who voted for either warmer or cooler temperatures (100 -

F_MCI_2). The thermal control hypothesis predicts that this percentage should decrease in

buildings where the degree of perceived control increases, but as seen in Figure 3.32, the

RP-884 database offers no empirical support for this hypothesis.



All Buildings

0

20

40

60

80

100

0 1 2 3 4 5 6 7mean pcc_ag

% w

antin

g ch

ange

(1

00 -

f_m

ci_2

)

(100 - f_mci_2) = 47.33 + 2.02 * pcc_ag

R2 = 0.05, p = 0.0237

Figure 3.32: Regression analysis between the percentage of building occupants requesting a change in temperature (100- f_mci_2) and the mean level of perceived control (pcc_ag) for the building.

3.3.3. Building occupancy types - offices, residential and industrial

Another corollary of the adaptive hypothesis is that the thermal perception of a particular set

of thermal environmental factors is determined, in part, by the physics of the body’s heat

balance, but also by the functional context of the building setting. That is, perception of a

given state of body heat balance may differ, depending on the setting, because the

occupants’ expectations are context specific and as such, not directly transferable from, say,

the office setting to residential. In order to explore these issues in the RP-884 database,

building function was classified within the database using the information supplied by the

original researchers (Appendix C). A simple three-fold classification consisted of 1)

residential, 2) office, and 3) industrial.

Table 3.18 presents the summary statistics for each of the main thermal environmental

parameters across all three functional classes of building in the RP-884 database.

Obviously the overwhelming majority of buildings in the database were offices and so the

analyses and conclusions developed in earlier sections of this chapter apply primarily to this



class of building. However, the small sample of residential buildings summarized in Table

3.18 permit some comparisons to be drawn with office buildings. Firstly, the percentage of

physical measurements of indoor climates actually meeting the ET* recommendations of

ASHRAE Standard 55-1992 was remarkably low for the 16 residential buildings in the

sample, ranging from an average of 6% in summer to 21% in winter. These low compliance

levels mainly resulted from the high mean indoor summer temperature of 30°C and low

indoor temperature means of 19°C in winter. Table 3.18 also indicates that mean indoor air

speeds were generally higher in residential buildings compared with office and industrial

settings, and they also showed a much larger seasonal variation in the residential cases.

While mean metabolic rate estimates indoors remained relatively constant across

residential and office settings at about 1.2 met units, they were noticeably higher in the small

number of industrial buildings included in the RP-884 sample, with means ranging between

2 and 2.5 met units. The seasonal swing in mean building occupant thermal insulation levels

was relatively small in the case of office and industrial buildings, amounting to less than 0.2

clo units. However, there was a much larger seasonal adjustment of insulation means

across the residential buildings in the sample, suggesting that clothing adjustment

represents a more powerful adaptive response in the home than in the workplace.



Table 3.18: Summary of the thermal environmental conditions in three classes of building included in the RP-884 database.

residential summer winter

offices summer winter

industrial summer winter

number of buildings in sample$

12

4

98

38

4

4

mean (±stdev) * operative temp (°C)

30.2 ±0.932

18.8 ±4.86

24.3 ±2.07

22.6 ±0.74

22.6 ±1.40

20.4 ±1.64

mean (±stdev) relative humidity (%)

43.9 ±17.0

45.9 ±13.3

53.2 ±9.1

32.8 ±10.5

48.9 ±3.4

43.7 ±8.3

mean (±stdev) % compliance with ASHRAE Standard 55@

6.4 ±7.4

20.9 ±13.0

55.8 ±30.6

86.5 ±16.6

0 ±0

0 ±0

mean (±stdev) air speed (m s-1)

0.31 ±0.10

0.15 ±0.04

0.13 ±0.07

0.08 ±0.03

0.06 ±0.00

0.06 ±0.002

mean (±stdev) insulation (clothes+chair) (clo)

0.58 ±0.16

1.34 ±0.16

0.70 ±0.075

0.89 ±0.16

0.66 ±0.06

0.82 ±0.08

mean (±stdev) metabolic rate (met)

1.20 ±0.08

1.12 ±0.04

1.20 ±0.10

1.17 ±0.05

2.54 ±0.08

2.14 ±0.573

* Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to

the way the data were originally supplied to the RP-884 database @ Percentage of physical observations within each building falling between ASHRAE Standard 55-92 ET*

limits for the relevant season

Having summarized the physical environmental and behavioral factors in three classes of

building in Table 3.18, the main task of Table 3.19 is to summarize the subjective thermal

responses to those indoor climatic conditions, again for residential, office and industrial

settings. It appears that the samples of residential building occupants were, on average,

less than half as sensitive to indoor temperature as the office building samples, since the

gradient of their thermal sensation votes with respect to indoor operative temperature was

about one vote per every 3~5 K change in temperature. In comparison the statistic from the

sample of office buildings was closer to one sensation unit to every two degrees. Another

noteworthy comparison between building function in Table 3.19 concerns acceptability

ratings of buildings. Despite the very low level of ASHRAE Standard 55 compliance in the

residential buildings in the database (Table 3.18), their acceptability ratings, at least in

summer, were not appreciably lower than those registered in office buildings where the

Standard 55 compliance levels were a good deal higher. Even in winter the acceptability

ratings in residential sample buildings dropped only about 10% below the office buildings’

average rating, whereas the ASHRAE Standard compliance levels dropped by over 60%



from office to residential settings. The implication of these comparisons is clearly that

contextual factors have a strong bearing on how a given set of indoor thermal environmental

parameters will be perceived by the occupants.

Table 3.19: Summary of the subjective thermal responses across the three classes of building included in the RP-884 database.

residential summer winter

offices summer winter

industrial summer winter

number of buildings in sample$

11

3

66

21

1

0

mean ±stdev thermal sensitivity (i.e. regression gradient (sensation vote/deg K top)

0.20

±0.130

0.13

±0.066

0.47

±0.242

0.44

±0.197

0.35 n.a.

n.a. n.a.

mean ±stdev thermal neutrality (°C top)

25.66 ±2.176

24.41 ±1.356

24.11 ±1.627

22.03 ±1.335

19.23 n.a.

n.a. n.a.

mean ±stdev % casting acceptable thermal sensation votes (i.e. -1.5 < ASH < +1.5)

81.21

±8.756 n = 12

66.70

±38.012 n = 4

79.17

±11.239 n = 98

78.00

±16.046 n = 38

28.78

±17.494 n = 4

55.20

±11.827 n = 4

* Mean and stdev figures quoted are averages across the n buildings in the table’s cell $ Note that in some studies large samples across several buildings have been treated as one building due to

the way the data were originally supplied to the RP-884 database. Also, sample size is based only on statistically significant regression models, except where otherwise indicated (i.e. n=...)

n.a. “not applicable”

3.4. Summary of basic results

This chapter has presented a complex array of findings, exploring different thermal indices,

different dimensions of subjective comfort, the effects of different seasons and climates,

different modes of indoor climate control, and different patterns of building occupancy. This

final section summarizes and interprets the key findings in relation to the adaptive

hypothesis of thermal perception. This synthesis provides the starting point for developing

more complex adaptive models in the next chapter.

3.4.1. Summary of thermal sensation, acceptability and preference

Subjective thermal comfort research has been unfortunately complicated over the last thirty

or forty years with the adoption of several different constructs of thermal perception. This



chapter dealt with three of these -- thermal sensation, thermal acceptability and thermal

preference. Sensation appears to be the most universally used version of questionnaire

scale, and certainly the most ubiquitous within the RP-884 database. Consequently the

more complex and abstract statistical analyses in this project were necessarily confined to

this expression of thermal comfort. However, there was a useable quantity of data on

thermal acceptability and preference within database as well, permitting several

observations to be made about the semantic similarities and differences between all three

constructs and the implications for practical applications.

A very clear observation that emerges from the RP-884 analyses of direct assessments of

thermal acceptability is that building occupants’ responses to direct questions such as this:

“Is the thermal environment in this building at the moment acceptable to you or not?”

bear virtually no relationship to the objective, physical conditions prevailing within the

building at the time of the questionnaire. Evaluations of RP-884 database buildings’ indoor

climatic quality in terms of its compliance with the relevant summer or winter temperature

prescriptions of ASHRAE Standard 55 were completely dissociated from the direct

acceptability ratings of those same buildings by their occupants. We therefore regard

questionnaire items on direct thermal acceptability as being too ambiguous and vague to be

of any practical value in thermal comfort research or practice.

While direct ratings of thermal acceptability for indoor climates may not be particularly useful,

there remains a practical need for information about the range of temperatures which can be

regarded as acceptable for a given building in a specific climatic context. Accepting

Fanger’s (1970) assumption that a mean sensation vote of ±0.85 corresponds with 80%

thermal acceptability (or ±0.50 corresponds with 90%), it was possible in Section 3.1.3.4 to

extract from the database ranges of acceptable temperatures within each of the sample

buildings. ASHRAE Standard 55 suggests operative temperature ranges between 3K and

3.5 K. The RP-884 database, on the other hand, indicated that only a 2.5 K range was

acceptable, on average, within HVAC buildings. In NV buildings, however, the 90%

acceptable range extended significantly further, with an average of 5 K. This stretched to 7

K for the less stringent 80% acceptability criterion.



The actual range of acceptable operative temperatures within any particular building was

found to depend to a large extent on the degree of indoor climatic variability measured within

that building (r=+0.66). This relationship suggests that if, through prior experience,

occupants of a building come to expect considerable thermal variability, the range of

temperatures regarded as acceptable will extend accordingly.

Compared to direct thermal acceptability ratings, thermal sensation rating scales showed a

much more consistent pattern of association with indoor thermal environmental indices.

Correlations within each building in the database were statistically significant in the majority

of cases in Appendix A, and this permitted the derivation of thermal neutralities wherever the

sample of building occupants was large enough. Thermal neutrality is defined as that value

of a thermal index (TOP, ET, SET or PMV) corresponding with a mean thermal sensation

rating of “neutral” by the building’s sample of occupants. Assuming that neutrality is

synonymous with the “optimum thermal condition” for a particular building, it should be more

useful than direct acceptability ratings as a basis for application and practice.

The temperatures which building occupants felt to be neutral were broadly similar in both

HVAC and NV buildings, coming in at about 24°C in summer and 22.5°C in winter (TOP or

ET). These figures approximate the centre of ASHRAE Standard 55’s summer and winter

comfort zones. Neutrality defined in terms of the fully developed heat balance index such as

SET also fell within the same range. Thermal neutrality depended on mean temperatures

within both HVAC and NV buildings, but the rate of change of neutrality with respect to mean

building operative temperature was twice as steep in NV buildings as it was in HVAC

buildings. This finding suggests that occupants of NV buildings were twice as adaptable in

terms of making themselves feel neutral than their counterparts in HVAC buildings.

Fanger’s PMV model seemed to be reasonably accurate at predicting building neutralities

across the whole sample of buildings, with an average prediction error less than half a

degree (compared to observed neutralities). However, the standard deviation of the

prediction error between buildings was quite high at 3.8 K. This suggests that, while the

model worked well across a large sample of buildings, its predictions within any single

building could be significantly wrong. Assuming that the quality of input data in the RP-884

database is of a uniformly high standard (Class 1 and II studies only), the explanations for the



PMV model’s building-specific prediction errors must lie in non-thermal factors beyond the

human heat balance. For example, the average prediction error in NV buildings was nearly

a full degree, and its between-building standard deviation exceeded 5 K, suggesting that

contextual factors degraded the model’s predictive powers.

Preferred temperatures (as distinct from neutral or acceptable temperatures) could only be

derived within only a subset of the RP-884 building sample. Preferences were found to

occur within the 21~27°C range in most buildings. The average semantic discrepancy

between neutral and preferred temperatures in buildings was generally within half a degree.

While the sign and magnitude of the semantic discrepancy was unrelated to mean warmth

within naturally ventilated buildings, Section 3.1.5 reported a significant tendency for

neutrality to diverge away from preferred temperature within the sample of HVAC buildings

(r=+0.50) as mean temperatures within buildings departed from 22.5°C.

3.4.2. Summary of thermal sensitivity and behavioural thermoregulation

The linear dependence of thermal sensation votes and indoor climate showed a complex

pattern of differences between HVAC and NV buildings, and also between the various

indices of indoor climate (Section 3.1.1). Using the simpler indices such as TOP and ET,

we found that persons in centrally controlled HVAC buildings were, on average, more than

twice as sensitive to changes in temperature as their counterparts in naturally ventilated

buildings. However, this heightened sensitivity diminished when the more complex heat-

balance indices of warmth such as PMV and SET were used, with a sensation category

having a fairly constant temperature width of about four degrees. One interpretation is that

occupants of naturally ventilated buildings behaviorally regulate their heat balance with

clothing and air speed adjustments such that they can accommodate wide variations in

temperature indoors without adverse impacts on thermal sensation -- that is, they are

actively thermoregulating their sensations. In contrast, occupants of centrally heated and air-

conditioned buildings seem less adaptive behaviorally, and as a result their thermal

sensations appear more sensitive to excursions of indoor temperature away from average,

expected set-points.

This interpretation is further supported by the clear relationships between behavioral factors

(clothing and air speed) and indoor temperature (Section 3.1.6). While the seasonal mean



insulation (clothes plus chair) levels were broadly similar in NV and HVAC buildings, there

was considerably greater variability within and between the NV buildings. Also, there was a

much higher correlation between insulation and indoor temperature in NV buildings (r= -

0.81) compared to HVAC buildings (r= -0.42). The data for indoor air speeds lends

additional support to this interpretation, with the summer average being twice as high in NV

as in HVAC buildings, and the variability within NV buildings also being greater.

Furthermore, the closer correlation between indoor air speed and indoor temperature in NV

buildings (r=+0.73) compared with HVAC buildings (r=+0.58) reinforces the conclusion that

occupants of naturally ventilated buildings were behaviorally more active in thermoregulating

their thermal sensations than were their counterparts in HVAC buildings.

3.4.3. Summary of the effects of outdoor climate on thermal perception indoors

The temperatures found to be neutral within both HVAC and NV buildings varied, depending

on season, with significantly warmer neutralities (defined in terms of operative temperature)

occurring in summer compared to winter. These seasonal differences became less

consistent as the thermal index used to define neutrality increased in complexity (PMV and

SET), but this may simply result from the climatologically inaccurate definition of “summer”

and “winter” applied throughout the database.

Parameterizing outdoor climate simply as the mean of daily maximum and minimum

effective temperatures (in shade) provided a more rational basis for exploring these effects

in Section 3.2.1. Thermal neutrality within buildings was found to correlate positively

(r=+0.65) with mean outdoor ET. While the strength of correlation was roughly comparable

between HVAC and NV buildings, the slope of the linear relationship was not -- indoor

neutrality was about twice as responsive to outdoor temperature in naturally ventilated

buildings compared to air-conditioned. This difference suggests that much of the

adaptability observed in free-running buildings, described earlier as being driven by

expectations of warmth indoors, may in fact be driven by outdoor climate. Obviously indoor

and outdoor temperatures are highly correlated in naturally ventilated buildings (r=+0.91,

compared to r=+0.53 in HVAC buildings), so the temptation to include both in a multiple

regression model of thermal neutrality must be resisted if the stability of regression

coefficients is to be maintained.



As for explaining why thermal comfort adaptability might be related to outdoor climate, the

role of behavioral adjustments was the first place to look (Section 3.2.4). In particular, mean

clothing insulation worn inside buildings, both HVAC and NV, was found to correlate

negatively with mean warmth in the outdoor environment (r=-0.63). Mean air speeds inside

buildings were also found to be, correlated with outdoor warmth, but much more so in the

case of NV buildings (r=+0.80 compared to r=+0.44 in HVAC). It was also clear that the

range of mean air speeds found within naturally ventilated buildings (often exceeding

0.4~0.5 m/s) was much wider than in HVAC buildings where it rarely exceeding the 0.2 m/s

mandated in ASHRAE Standard 55 (1992).

The combined effect of these behavioral thermoregulatory processes and their relationships

with outdoor climate were examined in 3.2.1.4 where building neutralities predicted by the

heat-balance index PMV were regressed on mean outdoor ET. The simplistic description of

the PMV index as a “static” model throughout much of the adaptive comfort literature

(reviewed in Chapter 1) was clearly not supported in this analysis, because observed

regression equations were statistically significant and positive in both HVAC and NV

samples.

3.4.4. Summary of the effects of contextual factors and perceived control

The RP-884 index of perceived thermal control comprised a check-list of specific adaptive

opportunities and their relative efficacy which we applied to each of the buildings in the

database. The index clearly differentiated mixed-mode buildings from naturally ventilated

buildings as affording their occupants the greatest degree of thermal control, largely due to

their provision of both thermostats and operable windows. Naturally ventilated buildings

came up second in average control index rankings, while the centrally-controlled HVAC

buildings scored worst on the index. Despite the ability of the index to differentiate the three

type of building in the RP-884 database, we found it had no correlation with thermal

acceptability, sensitivity or preferences.

Rather than interpreting this as a categorical negation of the role of perceived control in

thermal perception, we think there are at least two alternative explanations:



1. The validity of the index itself is flawed. The perceived control scale was constructed very

simplistically, mainly due to the nature of the raw data supplied to the RP-884 database.

2. Alternatively, the effects of perceived control may not have such a simple and direct

relationship with thermal perception. The constructs of perceived control and adaptive

opportunity within buildings may in fact exert more complex effects on thermal perception,

and as a result, be statistically significant once other dimensions of indoor and outdoor

climate have been taken into account. The possibility of complex, interactive effects of

the pcc_ag index on thermal perception will be explored further in Chapter 4.

The functional classification of RP-884 sample buildings into office, residential and industrial

uncovered sharp differences in the basic indoor thermal environmental parameters such as

air speed and temperature. The three classes of building were also clearly differentiated in

terms of their compliance with effective temperature index limits within ASHRAE Standard

55. For example, a majority of the observations made inside office buildings, regardless of

whether they were air-conditioned or not, complied with the ASHRAE Standard 55’s ET*

limits, whereas the typical residential or industrial building scored less than 20% compliance

with the standard. We also observed distinct differences in the degree of behavioral

thermoregulatory adjustment made by residential building occupants compared to office

workers. For example, seasonal clothing insulation contrasts were sharper in the residential

as opposed to office setting.

Despite these obvious differences in physical and behavioral features of indoor climate for

office and residential buildings in the sample, we couldn’t discern sharp differences in

occupant evaluations of the buildings’ indoor climatic quality. Despite their relatively poor

performance on the objective physical indoor climatic criteria, occupants’ thermal

acceptability ratings for residential buildings were comparable to those within office

buildings. The strength of this contextual effect on subjective response is no doubt part of

the explanation for the lack of any statistical correlation between thermal acceptability

responses and indoor or outdoor climatic indices, as noted in Section 3.4.1 of this chapter.


__________________________________________________________________________________ Towards Adaptive Models page MRL Australia 139

CHAPTER 4 -- TOWARDS ADAPTIVE MODELS

The preceding chapter presented field evidence for the effects of indoor and outdoor

climatic factors on the way building occupants perceive the environments provided by their

buildings. Contextual factors such as whether the building is residential or work-place, and

how much adaptive opportunity it affords were also investigated. The aim of this chapter is

to develop these themes into adaptive models of thermal comfort, relating them back to

previous research on the topic, as reviewed in Chapter 1. These adaptive models will form

the basis of a variable temperature standard for indoor climate to be proposed in the next

chapter.

The reader should note that we have tried to make the terminology of equations in this and

subsequent chapters more descriptive than the nomenclature of earlier chapters.

4.1. The semantics of thermal comfort

Chapter 3 demonstrated that the indoor temperature regarded by building occupants as

“neutral” did not always coincide with that which they rated as most “acceptable” or

“preferable.” Evidence was presented for a “semantic artefact” which caused neutrality

(derived from thermal sensation) to be displaced to the right of preference (warmer) in hot

climates, and to the left of preference (cooler) in cold climates. In other words, in hot

climates people preferred a thermal sensation slightly cooler than neutral, while in colder

climates they preferred to feel slightly warmer than neutral.

Earlier researchers have found similar semantic effects -- de Dear et al. (1991a) recorded a

group-mean thermal sensation of -0.33 for their Singaporean climate chamber subjects

while they were seated in their self-determined preferred temperature (i.e. they preferred to

feel cooler than neutral). This is the equivalent of one whole degree (K) in semantic offset for

the clothing, metabolic rate and air speed in question. Oseland (1994a,b) also reported

semantic discrepancies between preference and thermal sensations, finding that they were

stronger in their winter study where subjects decidedly preferred thermal sensations that

were slightly warmer than neutral. The extent to which culture and climate affect people’s



thermal preferences, and the semantics they use to describe them, has also been discussed

at length by McIntyre (1978a, 1978b, 1982).

A test of this semantic artefact hypothesis within the RP-884 database appeared in Figure

3.24 as a set of graphs relating the discrepancy (discrep) between neutrality and preference

within each building to the mean level of effective temperature in outdoor climate. There we

found a significant linear correlation of r=+0.62 between discrep and mean outdoor effective

temperature (dayavet) within the HVAC (and a few mixed-mode) buildings in the database.

The linear equation indicated that neutrality and preference coincided only in those HVAC

(and mixed-mode) buildings located in climates where the mean outdoor effective

temperature was 13.6°C.

semantic effect = -0.95 + 0.07 * mean outdoor ET* for HVAC buildings eq. 4.1

In climates warmer than this, indoor preference became progressively cooler than neutrality,

while in regions where mean outdoor effective temperature fell below 13.6°C, preferred

temperature was warmer than neutral temperature. Clearly this semantic effect needs to be

accounted for when we develop variable temperature standards for HVAC buildings in the

next chapter.

In contrast to the situation just described for HVAC (and a few mixed mode) buildings, there

was no empirical evidence in the RP-884 database for a semantic effect within naturally

ventilated buildings (Figure 3.24). Exactly why people use words like “slightly warm” or

“slightly cool” differently in different types of buildings remains unclear at this stage.

Whatever the interpretation, it seems reasonable to develop a variable temperature

standard for naturally ventilated buildings exclusively on the basis of thermal neutrality, as

derived from rating scales such as the ASHRAE and Bedford 7-pt scales, ignoring the

semantic offset altogether.

The implications of the semantic effect on the HVAC building adaptive model can be

depicted graphically in Figure 4.1. There the “adaptive model” represents the thermal

neutrality function with respect to outdoor temperature from chapter three minus the

semantic effect just discussed.



buildings with centralized HVAC

-5

0

5

10

15

20

25

30

-5 0 5 10 15 20 25 30 35


com

fort

tem

per

atu

re (

oC

)

-5

0

5

10

15

20

25

30

sem

anti

c ef

fect

(K)

neutrality

semantic effect

adaptive modelincluding semantics

Figure 4.1: An adaptive model for HVAC buildings that accounts for the semantic offset between neutrality and preference.

4.2. Comparison of RP-884 models with earlier adaptive model publications

Indoor Climate: As noted in Chapter 1 (literature review), Humphreys (1975, 1978; 1981)

published various statistical models of the adaptive dependence of indoor thermal neutrality

on mean indoor and outdoor temperature. His statistical analysis of thirty six Class III studies

from various countries around the world revealed a clear dependence of thermal neutralities

(roughly equivalent to neut_top in RP-884 nomenclature) on the mean levels of air or globe

temperature (roughly equivalent to top in RP-884) recorded within the buildings (Humphreys,

1975):

neutrality = 2.56 + 0.83 * operative temperature (r=+0.96) eq 4.2

The RP-884 adaptive model that is most comparable to this equation of Humphreys’ can be

found in Figure 3.1 of the preceding chapter, where thermal neutrality (defined in terms of

operative temperature, neut_top) achieved weaker but still highly significant weighted

regression and correlation with building mean indoor operative temperature (top):




This RP-884 correlation coefficient can be improved slightly by deletion of all RP-884

industrial buildings and all Class III field studies from the analysis, since these were the clear

outliers throughout the Chapter 3 analyses. With a total of 11,620 subjects inside 98

separate buildings (HVAC plus naturally ventilation) remaining in the analysis, the following

statistically significant adaptive model was derived:


The RP-884 model in eq 4.4 indicates that thermal neutrality is barely half as sensitive to

mean building temperature compared to Humphreys’ model in eq 4.2. The RP-884 version

of the model indicates that neutral temperatures indoors increase by one degree for each

two-and-a-third degrees increase in mean indoor operative temperature, whereas the

comparable figure in Humphreys’ eq 4.2 approaches unity (one degree per 1.2 K of indoor).

Possibly the difference can be accounted for by the varying compositions of the two building

samples. Section 3.1.2.1 in the last chapter demonstrated a significant difference in the

sensitivities for HVAC and naturally ventilated buildings, so a different balance in the

composition of the all-building samples (HVAC plus natural ventilation) that were used to

generate eqs. 4.2 (Humphreys) and 4.4 (RP-884) would logically affect the resulting models’

gradients.

Outdoor Climate: Auliciems (1983) reanalysed Humphreys’ database of Class III field

research after deleting some suspect data and including new Class III studies that had been

published post Humphreys. The revised database included a total of 52 Class III field

studies. Fortunately the Auliciems (1983) paper included a table of fundamental statistical

data from each study in his database, including thermal neutrality (based on indoor air

temperature), mean monthly outdoor temperature (based exclusively on air temperature), as

well as an indication of whether the buildings in which the studies were conducted had

central HVAC systems or were naturally ventilated (see Appendix G). We have reanalysed

Auliciems’ database for the purpose of comparison with ASHRAE RP-884 and the resulting

regression models are presented below in Figure 4.2.

The models in Figure 4.2 indicate a disparity between the regression models for HVAC and

NV buildings, with the HVAC model having the smaller gradient. The statistical significance



of this difference was confirmed using the technique described in Kleinbaum et al. (1988)

(T=3.89, df=48, p<0.05). This begins to suggest that people in naturally ventilated buildings

are more connected to the natural swings and cycles in outdoor climate, and their optimum

thermal comfort conditions are more strongly influenced by these experiences.

All Buildings - Auliciems' Data

neut_ta = 0.31 * ( mean month outdoor temp) + 17.6R2 = 0.77

16

18

20

22

24

26

28

30

32

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

mean monthly outdoor air temperature (oC)

neut

ral i

ndoo

r ai

r te

mpe

ratu

re (

oC

)

Central Heated/HVAC Buildings-Auliciems' Data

neut_ta = 0.19 * (mean month outdoor temp) + 19.0

R2 = 0.48

16

18

20

22

24

26

28

30

32

0 4 8 12 16 20 24 28 32mean outdoor air temperature (oC)

neut

ral i

ndoo

r te

mp

(oC

)

Naturally Ventilated Buildings-Auliciems' Data

neut_ta = 0.52 * (mean month outdoor temp) + 12.3

R2 = 0.89

16

18

20

22

24

26

28

30

32

0 4 8 12 16 20 24 28 32

mean outdoor air temperature (oC)

neut

ral i

ndoo

r te

mp

(oC

)

Figure 4.2: Reanalysis of Auliciems’ (1983) Class 3 field study database (Appendix G) for the effects of outdoor climate on thermal neutrality

The RP-884 adaptive models that are most comparable to Auliciems’ in Figure 4.2 are

those based on the linear relationship between building neutralities and mean outdoor daily

effective temperature (Figure 3.18). Clearly both the RP-884 and Auliciems results support

the adaptive hypothesis inasmuch as both models depict a positive dependence of indoor



thermal neutrality on temperature outside the buildings surveyed, although the “all buildings”

RP-884 model achieved a lower overall correlation coefficient (r=+0.64 compared to

r=+0.87 in Auliciems). Another difference is that the gradient for the “all buildings” model in

RP-884 (Figure 3.18) was only half that found using the Auliciems database (b=0.16 as

opposed to b=0.31). It should be noted, however, that this may not necessarily mean that the

RP-884 building occupants were that much less climatically adapted than their counterparts

in the Auliciems database -- the divergent gradients could simply be an artefact of the

different indices used to represent outdoor climate in the two databases. Auliciems used

outdoor data based on climatological (30 year mean) monthly outdoor air temperatures,

whereas the RP-884 index was based on observed 2-node effective temperature (ET*)

outdoors. The reason this might have an effect is because the 2-node model’s ET* index

quantifies the incremental thermal impacts of elevated humidity. If the observations from

warm and humid climates in Auliciems’ database were transformed from simple air

temperatures into ET*, they would be non-uniformly displaced to the right along the abscissa

in Figure 4.2, with the effect being most pronounced for the warmer temperatures. These

effects, if incorporated into Auliciems’ database, could be expected to depress his

regression model’s gradient towards the slope found in the RP-884 analysis.

Despite being conceptually comparable, the Auliciems and RP-884 approaches to outdoor

climatic adaptive models have fundamental differences which dissuade us from simply

pooling the two databases together. These include:

• Internal consistency for data going into the RP-884 database was more rigorously

controlled. For example, the dependent variable, thermal neutrality, was recalculated from

raw data by us rather than relying on those published by the original researchers.

• The RP-884 outdoor climatic index (ET*) included humidity effects which, as noted

earlier, were ignored in the Auliciems database.

• The RP-884 indoor climatic index, operative temperature, included mean radiant

temperature effects which would be overlooked by simple air temperature

measurements, as used in the Auliciems database.



• The unit of analysis in Auliciems’ database was the field study, whereas we analysed data

at the level of individual buildings. It is felt that potentially significant contextual effects

may have been glossed over at the former level of data aggregation.

• The ranges of predictor variable, outdoor climate, covered by the two databases were

significantly different. Centrally heated, ventilated and air-conditioned buildings were

observed in climate zones ranging from mean outdoor temperature -5°C⇔ +33°C in the

RP-884 database, whereas Auliciems’ database only covered the range 0°C⇔ +23°C.

Naturally ventilated buildings were observed in outdoor climates ranging from mean

temperature +5°C⇔ +33°C in the RP-884 database, whereas Auliciems’ database only

covered the range +14°C⇔ +33°C. Therefore, the wider range of climates within the RP-

884 database encourages their extensive application across diverse climate zones

around the world.

Apart from these methodological differences between databases, another factor dissuades

us from calculating an RP-884 multiple regression model for comparison with the Auliceims’

combined indoor-outdoor adaptive model (1983):

neutrality = 9.22 + 0.48 * mean indoor temp + 0.14 * mean outdoor temp eq 4.5

High correlations between the “independent” indoor and outdoor temperature variables

throughout the RP-884 database (weighted Pearson’s r=+0.66, p=0.0001) would render any

regression coefficients within multiple regression models unstable. This would have the

effect of making comparisons with Auliciems’ model in eq. 4.5 unreliable (Michael

Humphreys, pers. com. BRE meeting UK, 1993).

4.3. Comparison of RP-884 models with the PMV “static model”

Section 3.2.1.3 indicated that the so-called “static model” of thermal comfort, PMV,

predicted not-so-static comfort temperatures for the buildings in RP-884’s database. In

particular, building occupants’ behavioral manipulations of heat-balance factors such as

clothing and air speeds showed a systematic dependence on outdoor climate. That is,

clothing insulation decreases in warm climates while air speeds increase. Obviously the

adaptive opportunity for manipulating these parameters is context specific, so comparisons



between the PMV predictions and RP-884 database observations need to be

disaggregated to HVAC and NV sub-samples.

4.3.1. Comparisons within the centrally conditioned building sample

The “static” versus adaptive comparison for RP-884’s HVAC buildings in Figure 4.3 shows

that comfort temperatures, after correction for semantic effects, have only a moderate

variation (less than 2 K) across a very wide range of outdoor climates (spanning about 40

K). An interpretation of this finding could be that occupants of such buildings have become

finely adapted to the mechanically conditioned and static indoor climates being provided by

centralized HVAC services. The question of “what type is this adaptation?” can be

answered by comparison with the comfort temperatures predicted by the so-called “static”

model (PMV). The two models appear very close together in Figure 4.3, with the

discrepancy being a 0.1 K offset in their Y-intercepts. This discrepancy is neither statistically

nor practically significant. PMV, therefore, appears to have been remarkably successful at

predicting comfort temperatures in the HVAC buildings of RP-884’s database. A corollary

of this finding is that the relatively minor behavioral adjustments to clothing and room air

speeds observed for the occupants of HVAC buildings explain the systematic response in

comfort temperature to outdoor climatic variation, and that these adaptive behaviors are, in

fact, being accounted for by the PMV model.

Against this picture of general agreement between models, a subtle but nonetheless

important distinction between the PMV and RP-884 adaptive models deserves a mention.

The latter were based on thermal sensation data, after being corrected for semantic

artefacts, whereas the PMV model was based exclusively on thermal sensation data without

semantic considerations taken into account. While in practical terms the distinction may

seem trivial, what this means is that PMV successfully predicts optimum comfort

temperatures in field settings despite being intended to predict neutral temperatures. For

these reasons we have labelled the Y-axis in Figure 4.3. as “comfort temperature” instead of

“thermal neutrality.”



buildings with centralized HVAC

20

21

22

23

24

25

-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (

oC)

co

mfo

rt t

em

pe

ratu

re (o C

)

RP-884 adaptive modelwith semantics

"static" model (PMV)

Figure 4.3: Comparison of the RP-884 adaptive model (based on observed neutralities corrected for semantic effects) and the “static” model (based on PMV predictions) for HVAC buildings.

It is interesting to note that this graph so closely matches predictions of PMV with

observations in real HVAC buildings, whereas so many of the earlier thermal comfort field

research papers which we discussed in Chapter 1’s literature review indicated quite the

opposite. Indeed, some of those anomalous papers were from authors who contributed their

raw data to this project’s database. Therefore our success at bringing PMV predictions

into line with observations in HVAC buildings most probably can be attributed to the quality

controls and precautions we took when assembling the RP-884 database, which

transformed, to some extent, the raw data used in the authors’ original analyses. Among the

more important of these were probably:

• setting minimum standards on instrumentation and protocols for data going into the RP-

884 database,

• conversion of all clo estimates throughout the entire database to a single standard

(ASHRAE 55-92),

• inclusion of the thermal insulation effects of the chairs used by subjects (McCullough and

Olesen, 1994),

• recalculation of thermal indices from raw data throughout the entire database with a

consistent software tool (Fountain and Huizenga, 1995),



• application of a consistent set of statistical techniques to all raw data instead of relying on

different author’s approaches to thermal neutrality, preference and other statistically

derived parameters,

• conducting the meta-analysis at the appropriate scale of statistical aggregation, namely

the individual building.

We are therefore led to the conclusion that Fanger’s PMV model is, in reality, an “adaptive”

model which is suitable for application as it was initially proposed back in 1970 by Fanger

himself; as an engineering guide in HVAC buildings the world over1. The main sticking point

with its application in predictive mode before a building is constructed, or occupied, is that it

is unusual to have detailed observations on mean clo values or air speeds within a building

at the design stage. The practical solution here is to seek further guidance from the RP-884

database. We know from Figure 3.25 that the thermal insulation (in clo units, clothes plus

chair) applicable to PMV calculations is highly correlated with mean outdoor effective

temperature (dayavet):

thermal insulation = 0.93 * e-0.013*(mean outdoor ET*) (r= +0.80) eq 4.6

Figure 3.27 indicates that mean room air speeds (m s-1) within HVAC buildings are also

correlated with mean outdoor effective temperature:

mean room air speed = 0.08 * e+0.014*(mean outdoor ET*) (r= +0.44) eq 4.7

so it seems not unreasonable to anticipate the unknown inputs to PMV simply from a

knowledge of the outdoor weather/climate conditions for the site in question.

An even simpler approach is to directly predict PMV-based neutrality using the linear

regression model depicted in Figure 3.20. In effect this amounts to predicting the aggregate

effects of climate on clothing insulation and room air speeds within HVAC buildings.

1 In the introductory chapter to his book entitled “Thermal Comfort - Analysis and Applications in Environmental Engineering” which introduced the PMV model, Fanger was quite clear that the book, and by implication, the PMV model at its core, were intended for application by the HVAC industry in the creation of “artificial climates” in “controlled spaces.” The generalisation of the PMV model to all spaces intended for human occupancy, HVAC or NV, was a much later development that we disagree with.



Therefore, the adaptive model for HVAC buildings (noted here as “PMV, adaptive”) reduces

to this simple linear equation (from Figure 3.20):

comfort temperature in HVAC = 22.6 + 0.04 * mean outdoor ET* (r= +0.50) eq 4.8

So, it would appear that the occupants of buildings with centralized HVAC systems have

become adapted to the temperatures that they encounter within their buildings -- generally

within the narrow 22~24°C range. Of course this begs the question of whether or not it is

possible to extend the range of comfort adaptation by deliberately letting indoor HVAC

setpoints more closely track outdoor weather and climatic conditions? We concede that a

purposely designed intervention field experiment on a “real” building, would be the most

appropriate way to test this hypothesis. However, we can draw comparisons with the

naturally ventilated RP-884 sample where the rate of change in thermal insulation with

respect to variations in outdoor climate (Figure 3.25) was significantly greater than in

centrally conditioned buildings. Across the 5⇒ 30°C range of mean outdoor effective

temperatures in Figure 3.25, building occupants’ mean insulation (including chair effects)

varied by about 0.3 clo units in the HVAC sample, whereas the clothing response was more

than double this in naturally ventilated buildings across the same outdoor temperatures. In

short, naturally ventilated building occupants appear to be prepared to take on greater

personal responsibility for maintaining their thermal comfort, when required to. Whether they

would be prepared to do likewise if required in HVAC buildings remains a moot point

deserving further research.

The same line of reasoning can be applied to indoor air velocities in HVAC buildings. We

noted they were confined to very low levels (virtually still air, at <0.2 m s-1) within the RP-884

sample of HVAC buildings, almost regardless of outdoor climate (see Figure 3.27). This

stood in marked contrast to the naturally ventilated sample where within-building mean

velocities went up to 0.4 m s-1 for outdoor mean effective temperatures of about 30°C.

These velocities could possibly be feasible inside centrally conditioned buildings, perhaps

with supplementary air movement in the occupied zone provided by local fans or other

means for individual thermal control. Present-day HVAC building occupants appear

adapted to conditioned, still-air conditions, but they may be willing to more actively regulate



convective and latent heat losses if the behavioral opportunities to do so were made

available to them and HVAC set-points provided the stimulus (eg allowing for warmer indoor

temperatures during summer).

As noted above, new research is required to establish just how much thermoregulatory

responsibility occupants of HVAC buildings may be prepared to accept. Future field

experimentation may suggest that simply predicting PMV-based neutralities from a

knowledge of mean outdoor temperature, as in eq. 4.8 above, is inappropriate for the

purpose of establishing HVAC set-points. Instead, it may well be more appropriate to first

estimate likely indoor clothing insulation levels and air velocities from equations resembling

those established for the naturally ventilated sample in Figures 3.25 and 3.27 (instead of

the HVAC models presented in eqs. 4.6 and 4.7 above), and then iteratively solving the

PMV model for “neutral” operative temperature. Clearly further field experimentation on

these questions of thermal adaptation in HVAC buildings is required.

4.3.2. Comparisons within the naturally ventilated building sample

Figure 4.4 repeats the “adaptive” versus “static” comparisons for the naturally ventilated

buildings within the RP-884 database. One important departure from the method just

applied to HVAC buildings, however, is the omission of the semantic effect, as discussed in

Section 4.1. This is because we were unable to discern any systematic relationship

between the preferred and neutral temperatures for the naturally ventilated buildings

analysed in Figure 3.24.

The remarkable agreement found between PMV and adaptive models in the HVAC building

sample clearly breaks down in the context of naturally ventilated buildings where the adaptive

model shows a gradient almost twice as steep as the heat-balance PMV model’s. This

divergence tested positive using the Kleinbaum et al. technique (1988) (T=2.43, df=80,

p<0.05). It therefore appears as if behavioral adjustments to body heat balance (i.e.

biophysical effects) account for only about half of the climatic dependence of comfort

temperatures within naturally ventilated buildings. In effect, the PMV model has been

demonstrated to function as a partially adaptive model of thermal comfort in naturally

ventilated buildings.



However, there still remains the other half of the adaptive effect to be explained. Having

partialled out the effects of behavioral adaptations, we’re left with the physiological

(acclimatization) and psychological (habituation) hypotheses discussed in Chapter 1. There

we noted that effects of acclimatization were not in evidence during climate chamber

experiments on moderate heat/cold stress exposures, so it is not surprising that they failed

to reappear in the field settings analyzed in RP-884. Therefore, by a process of elimination,

we are left with psychological adaptation (i.e. expectation and habituation) as the most

likely explanation for the divergence between field observations and heat-balance (PMV)

predictions.

buildings with natural ventilation (no HVAC)

20

21

22

23

24

25

26

27

28

29

30

5 10 15 20 25 30 35mean daily outdoor effective temperature (

oC)

com

fort

tem

per

atu

re (o

C)

RP-884 adaptive model

"static" model (PMV)

Figure 4.4: Comparison of the RP-884 adaptive model (based on observed neutralities in Figure 3.18) and the “static” model (based on PMV predictions) applied to naturally ventilated buildings.

One might wonder why the laboratory-based PMV heat balance model works so well in RP-

884’s HVAC buildings but not so for the NV buildings? Perhaps we can regard the former

as being quite comparable to the climate chamber setting? In both climate chambers and

HVAC buildings the thermal environment is entirely regulated by processes outside the

person-environment feedback loop discussed in Chapter 1. Naturally ventilated buildings,

on the other hand, are much more “interactive,” with adaptive feedback loops being closed

at both behavioral and psychological levels.



4.4. Adaptive models for acceptable ranges of indoor temperatures

Preceding sections defined some simple adaptive models for predicting optimal comfort

temperatures indoors, but overlooked the question of what sort of temperature

inhomogeneity might be acceptable. We saw in Section 3.1.3.4, particularly in Figure 3.9, a

direct correlation between the range of acceptable operative temperatures within each

building and its internal temperature variability (standard deviation). The relationship

reached statistical significance only for the naturally ventilated buildings in the RP-884

sample where the following linear regression model achieved a correlation coefficient of r=

+0.51:

range of acceptable temperatures = 4.2 + 1.65 * (stdev of indoor temperature) eq 4.9

The failure of a similar model to reach significance within centrally conditioned buildings

reinforces the fundamental difference between the HVAC and natural ventilation contexts

discussed at length throughout in this report. In the naturally ventilated setting, it appears as

if building occupants extend their range of thermal acceptability to accommodate the range

of thermal variation expected within their buildings.

We propose the simple model in eq 4.9 as the adaptive approach to prediction of 80%

acceptable ranges within naturally ventilated buildings. But for many applications it simply

will not be feasible to anticipate the standard deviation of indoor operative temperatures for

a building that is either yet to be built or not fully monitored for any significant length of time.

Therefore a more practical alternative for prescribing acceptable indoor temperature ranges

may be to rely on the RP-884 observations, as described in Table 3.9. In HVAC buildings

the general comfort 80% acceptability criterion corresponded, on average, to a range of two

degrees (K) either side of the optimal comfort temperature. Tightening the acceptability

criterion from 80% to just 90% in RP-884’s HVAC building sample meant a narrowing of the

acceptable range to ± 1.2 K. In either case, the corresponding 80% and 90% ranges

observed in the naturally ventilated RP-884 sample were significantly wider, at ± 3.5 K and ±

2.5 K respectively.



If these acceptable ranges are going to be applied to adaptive models which predict

optimum indoor temperatures on the basis of outdoor climatic conditions, we need to

address the possibility that the acceptable ranges themselves are also dependent on

outdoor climate. For example, one might speculate that the acceptable range diminishes as

outdoor climate becomes hotter for the simple reason that indoor clothing insulation levels

also decrease with increasing outdoor temperature (see Section 3.2.4.1). A statistical

regression test of this possibility was performed by fitting a regression model to the

dependence of acceptable indoor temperature ranges on outdoor effective temperature,

and the results are reported in Table 4.1. It can be assumed that, if the regression model

turns out to have a statistically insignificant gradient term, the subsample’s mean acceptable

range (as described in Table 3.9) can legitimately be applied across all climate zones. As

seen below in Table 4.1, none of the acceptable range models achieved statistical

significance at the 95% confidence level, regardless of building type nor acceptability level.

Therefore, the variable temperature standards to be proposed in the next chapter can be

based on an optimal temperature predicted from outdoor climate, plus or minus a constant

acceptable temperature range for the building type in question, which does not vary with

climate.



Table 4.1: Assessment of the dependence of acceptable indoor temperature ranges on outdoor effective temperature.


buildings


number of buildings 108 (3 missing values)


number of buildings with thermal sensation regression models achieving 95% significance*

63

(58% of total)

33

(75% of total) Mean range of indoor temperatures based on the 80% acceptability criterion (K)

4.1

6.9

Regression model for the dependence of the 80% acceptable temperature ranges on outdoor effective temperature Statistical T-test for the regression gradient Statistical significance of T-test

y=3.08 + 0.05*x

1.81 p>0.05

y=6.28 + 0.03*x

0.36 p>0.10

Mean range of indoor temperatures based on the 90% acceptability criterion (K)

2.4

4.9

Regression Model for the dependence of the 90% acceptable temperature ranges on outdoor effective temperature Statistical T-test for the regression gradient Statistical significance of T-test

y=1.81 + 0.03*x

1.81 p>0.05

y=3.70 + 0.02*x

0.36 p>0.10

* Based on those thermal sensation (ASH) models in Appendix A (y=a + b*TOP) achieving 95% statistical significance or better

The next chapter in this report will summarize this chapter’s adaptive models into a pair of

variable temperature standards - one for application in HVAC buildings and another for

application in the naturally ventilated context.


Variable Temperature Standard page 155 MRL Australia

CHAPTER 5 - VARIABLE TEMPERATURE STANDARDS

The last remaining task for ASHRAE RP-884 is to propose variable temperature thermal comfort

standards. The statistical analyses and adaptive models in Chapters 3 and 4 were presented separately

for buildings with and without centrally controlled HVAC systems. It seems logical, therefore, to partition

this chapter’s variable temperature standards along the same lines. This distinction between centrally-

controlled HVAC buildings in which individual occupants have little or no control over their immediate

thermal environment, and naturally ventilated buildings in which occupants at least have control over

windows, is a unique feature of the ASHRAE RP-884 project. All thermal comfort standards to date

(see Chapter 1), both extant and proposed, regardless of whether they were based on so-called “static”

or “adaptive” models, have been promulgated as universally applicable across all types of building.

By not differentiating their contexts for application, earlier comfort standards are, in effect, extrapolating

from relationships established in centrally controlled HVAC settings to naturally ventilated contexts, and

vice versa. In contrast, a fundamental tenet of RP-884 has been that the indoor climates found in HVAC

and naturally ventilated buildings are not only quantitatively different, but also qualitatively different, and

as such, they require separate comfort standards.

The reader is requested to regard the two standards in this Chapter as self-contained documents. There

is, therefore, some duplication of definitions and related material across the two standards.

5.1. A variable temperature standard for application in buildings with centrally controlled

HVAC

5.1.1. Purpose

To specify the combinations of indoor space environment and personal factors that will produce thermal

environmental conditions acceptable to a majority of the occupants within centrally heated and air-

conditioned spaces.



5.1.2. Scope

• The environmental factors addressed are temperature, thermal radiation, humidity, and air speed; the

personal factors are those of activity and clothing.

• It is intended that all of the criteria in this standard be applied together, since comfort in the space

environment is complex and responds to the interaction of all of the factors that are addressed.

• This standard applies to general thermal comfort conditions and excludes local discomforts such as

draft, vertical thermal stratification, and radiant asymmetry.

• This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric

pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for

periods not less than 15 minutes.

• This standard does not address such non-thermal environmental factors as air quality, acoustics, and

illumination; nor other physical, chemical or biological space contaminants which may affect comfort or

health.

• This standard is intended for use in design of HVAC-systems, design of buildings, evaluation of

existing thermal environments, building ratings or labelling, and testing of HVAC system performance.

• The standard applies exclusively to indoor environments with HVAC systems over which the

occupants have no control. The occupants of such buildings are presumed to have no option to

open/close windows.

5.1.3. Definitions

adaptive model: A linear regression model that relates indoor design temperatures or acceptable

temperature ranges to outdoor meteorological or climatological parameters. Note that the range of

applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s

graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable).

adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or

scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally

air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while



naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant

offices typically afford high degrees of adaptive opportunity.

clo: a unit used to express the thermal insulation provided by garments and clothing ensembles, where 1

clo = 0.155 m2 K/W.

comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it

requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal

preference vote of “want no change”

environment, thermal: the characteristics of the environment which affect a person’s heat loss.

environment, acceptable thermal: an environment which at least 80% of the occupants would find

thermally acceptable.

humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction

of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it

equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure

(or density) of water vapor at the same temperature.

insulation, chair: incremental thermal insulation of chairs used by building occupants. The typical office

chair’s clo value is ~0.15 clo units. This effect needs to be included in overall thermal insulation estimates

for the PMV model to yield accurate results.

insulation, clothing (Icl): the resistance to sensible heat transfer provided by a clothing ensemble (i.e.,

more than one garment). It is described as the intrinsic insulation from the skin to the clothing surface,

not including the resistance provided by the air layer around the clothed body; it is usually expressed in

clo units. Clothing worn by people indoors is modified to a great extent by the season and outside

weather conditions. During the summer months, typical clothing in commercial establishments consists of

lightweight dresses, lightweight trousers, short or long sleeved shirts and blouses and occasionally a suit

jacket or sweater. These ensembles have clothing insulation values (Icl) ranging from 0.35 to 0.6 clo.

During the winter season, people wear garments constructed of thicker, heavier (ie. warmer) fabrics and

often add more garment layers to an ensemble. A typical indoor winter ensemble would have an Icl value

ranging from 0.8 to 1.2 clo. Where the outside temperature range does not vary a great deal from season



to season, people do not change the types of garments they wear year round as much as people who

experience extreme hot and cold climates. The (Icl) provided by clothing ensembles can be estimated by

summing the garment Iclu values as described in ASHRAE Standard 55-92 (1992).

insulation, garment (Iclu): the increased resistance to sensible heat transfer obtained from adding an

individual garment over the nude body. It is the effective increase in overall insulation attributable to the

garment and is usually expressed in clo units.

mean air speed (velocity): arithmetic mean of instantaneous air speed measurements within the occupied

zone, integrated over a period of not less than three minutes (m s-1).

mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET*

(assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day.

metabolic rate (met): rate of energy production of the body. Metabolism, which varies with activity, is

expressed in met units in this standard. One met is defined as 58.2 Wm-2 which is equal to the energy

produced per unit surface area of a seated person at rest. The surface area of an average person is about

1.8 m2. In today’s society, most people are occupied with light, primarily a sedentary activity level

corresponding to 1 to 1.6 met. Metabolic activity should be assessed for a period between 30 and 60

minutes before any thermal assessment is made. For more detailed values see ASHRAE Standard 55-

1992, ISO 7730, ISO 8996 or the ASHRAE Handbook of Fundamentals (1993).

neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a

maximum number of building occupants voting “neutral” on the thermal sensation scale.

preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the

categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation.

Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, does not necessarily

correspond with thermal neutrality.

PMV: Predicted Mean Vote is a thermal index derived from the heat-balance model of thermal comfort

developed by Fanger (1970). PMV predicts the mean thermal sensation of a large group of subjects

experiencing a thermal environment specified in terms of mean air and radiant temperatures, air speed,

humidity, thermal insulation and metabolic rate.



PMV, analytic: Predicted Mean Vote index calculated analytically from mean measurements or estimates

of the six primary comfort parameters: mean air and radiant temperatures, mean air speed, humidity,

clothing (+ chair) thermal insulation and metabolic rate.

PMV, adaptive: the RP-884 adaptive regression model that predicts optimum thermal comfort

temperature (thermal sensation corrected for semantics). The name “adaptive PMV” is used for the

model because it predicts essentially the same optimum operative temperature answer as the analytic

PMV approach, but uses mean outdoor effective temperature as the only input instead of the usual four

inputs (clo, met, rh and v) required by the analytic PMV method.

sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly

cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. An individual’s

ideal thermal comfort does not necessarily correspond with a thermal sensation vote of “neutral” (zero).

summer: operationally defined as the cooling season; climatologically defined for the purposes of this

standard as having a mean daily outdoor effective temperature of 25oC.

temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant.

temperature, dew point (tdp): [or ambient water vapor pressure (Pa)], the temperature at which moist air

becomes saturated (100% relative humidity) with water vapor (Psdp = Pa) when cooled at constant

pressure.

temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which

an occupant would exchange the same amount of radiant heat as in the actual nonuniform space.

temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an

occupant would exchange the same amount of heat by radiation plus convection as in the actual non-

uniform environment. Operative temperature is numerically the average of the air temperature (ta) and

mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr):

th t h t

h ho

c a r r

c r

= ++

( )( )

which typically equates to the arithmetic average of mean air and radiant temperatures.



temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity

which would cause the same sensible plus latent heat exchange from a person as would the actual

environment.

temperature, optimum operative: the operative temperature that satisfies the greatest possible number of

people at a given clothing and activity level. Due to the semantic offset between preferred and neutral

temperatures, optimum operative temperature in this standard does not necessarily correspond exactly

with thermal neutrality (i.e. optimum temperature is neutrality after correction for semantic offset).

temperature, thermodynamic wet bulb: (also called the Adiabatic Saturation Temperature), that

temperature at which water, by evaporating into air, can bring the air to saturation adiabatically at the

same temperature. The wet bulb temperature measured with an appropriate psychrometer can approach

the thermodynamic wet bulb temperature.

winter: operationally defined as the heating season; climatologically, for the purposes of this standard a

typical winter condition is assumed to have a mean daily outdoor effective temperature of 0oC.

zone, occupied: the region normally occupied by people within a space, generally considered to be

between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning

equipment.

5.1.4. Conditions for an acceptable thermal environment.

The conditions for an acceptable thermal environment shall be based on one of the following three

techniques, listed in descending order of preference:

• the analytic PMV method, as described in ISO 7730 (1994) , if mean clothing and metabolic rates

are known in advance, or

• the adaptive PMV method in which indoor optimum operative temperature is predicted from a

knowledge of outdoor effective temperature using RP-884 regression models, or

• the prescriptive method in which summer and/or winter comfort zones for either 90% or 80% thermal

acceptability levels are selected from the RP-884 psychrometric charts.



5.1.4.1 Analytic PMV Method

See the detailed procedures for estimation of the optimum temperature for a group of building occupants

described in ISO 7730 (1994). The only departure from the methods described there is the inclusion of

the incremental thermal insulation of the chair into the seated occupants’ overall thermal insulation.

Optimum operative temperature may be predicted by inputting measured or estimated values of insulation

(clothing + chair), metabolic rate, relative humidity, air speed and solving for the unknown operative

temperature by setting PMV = zero. Note that the actual group mean thermal sensation expressed by

building occupants under the optimum operative temperature predicted by this method may not

necessarily equal zero (“neutral”). This is due to the semantic offset between group thermal neutrality and

preference. Therefore PMV equal to zero may correspond with a non-zero mean thermal sensation for

the group of building occupants in question, but they will still be in their optimum operative temperature.

5.1.4.2. Adaptive PMV method

In HVAC situations where the mean thermal insulation (clothing and chairs) and mean air speed cannot be

observed or accurately anticipated, the adaptive PMV method may be applied. Weather data in the form

of mean outdoor effective temperature for the relevant time of year is required. In the absence of current

meteorological observation, published

mean climatological data for the relevant month from the nearest or most relevant weather station may

suffice.



18.0

20.0

22.0

24.0

26.0

28.0

-5 0 5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)

com

fort

tem

pera

ture

(o C

)

comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET*

80% acceptability lower limit

80% acceptability upper limit

Figure 5.1: The adaptive PMV comfort zone’s optimum and limits for an 80% acceptability level in HVAC premises.

18

20

22

24

26

28

-5 0 5 10 15 20 25 30 35


com

fort

tem

pera

ture

(oC

)

comfort temp. in HVAC = 22.6 + 0.04 * outdoor ET*



Figure 5.2: The adaptive PMV comfort zone’s optimum and limits for an 90% acceptability level in HVAC premises.



5.1.4.3. Prescriptive method

Where outdoor meteorological or climatological data are unavailable, the RP-884 prescriptive method

may be used to define acceptable ranges of temperatures. The prescriptions are designed to provide

environments in which minimum levels of thermal acceptability (based on general thermal comfort) can be

selected as either 90% or 80%.

0

5

10

15

15 20 25 30

OPERATIVE TEMPERATURE (oC)

HU

MID

ITY

MIX

ING

RA

TIO

(g

/kg

)

30% rh

19o C Wet Bulb

18o C Wet Bulb

100% rh

70% rh60% rh

50% rh

Winter

Summer

24.7 ET*21.3 ET*

0

5

10

15

15 20 25 30

OPERATIVE TEMPERATURE (oC)

HU

MID

ITY

MIX

ING

RA

TIO

(g

/kg

)

30% rh

19 o C Wet Bulb18 o C Wet Bulb

100% rh

70% rh60% rh

50% rh

Winter

Summer

25.5 ET*20.5 ET*

Figure 5.3: Psychrometric charts showing summer and winter comfort zone prescriptions for 90% acceptability (left panel) and 80% acceptability (right panel)

Operative Temperature. The operative temperature range between which, theoretically, no more than

20% of occupants during light, primarily sedentary activity (� 1.2. met), assuming they wear the same

level of clothing insulation, will find the environment thermally unacceptable is given in Table 5.1. The

acceptable range of operative temperatures and humidities for winter and summer is further defined on the

psychrometric chart of Figure 5.3. The comfort zones are:

a) Winter: to = 20.5oC to 24.5oC at 50% rh for 80% acceptability level.

to = 21.3oC to 23.7oC at 50% rh for 90% acceptability level.

The slanting side boundaries of the winter zones in Figure 5.3 are defined in terms of effective

temperature (ET*) lines and are loci of constant thermal sensations.

b) Summer: to = 21.5oC to 25.5oC at 50% rh for 80% acceptability level.

to = 22.3oC to 24.7oC at 50% rh for 90% acceptability level.

The slanting side boundaries of the summer zones in Figure 5.3 are defined in terms of effective

temperature (ET*) lines and are loci of constant thermal sensations.



The winter and summer comfort zones overlap in the 22oC to 23oC range. In this region people in

summer dress would tend to approach slightly cool sensation while those in winter clothing would be near

the slightly warm sensation. In reality, the boundaries of each zone are not as sharp as depicted in Figure

5.3 due to inter-individual clothing and activity differences.

Table 5.1: Optimum and acceptable ranges of operative temperature for persons engaged in light, primarily sedentary activity (� 1.2 mets) at 50% relative humidity and mean air speed � 0.15 ms-1. For use in buildings with central HVAC systems.

Description of Icl Operative Temperature Season typical thermal insulation clo optimum

temperature range (90% accept.)

range (80% accept.)

Winter

heavy slacks, long sleeve shirt, sweater and office chair

1.05

22.5 oC

21.3 - 23.7 oC

20.5 - 24.5 oC

Summer

light slacks, short sleeve shirt and office chair

0.65

23.5 oC

22.3 - 24.7 oC

21.5 - 25.5 oC

For infants, certain elderly persons, and individuals who are physically disabled, the lower limits of Table 5.1 should be avoided.



5.2. A variable temperature standard for application in naturally ventilated buildings

5.2.1. Purpose

To specify the thermal environmental conditions that will be acceptable to a majority of the occupants

within naturally ventilated spaces.

5.2.2 Scope

• The environmental factors addressed are temperature, thermal radiation, humidity.

• It is intended that all of the criteria in this standard be applied together, since comfort in the space

environment is complex and responds to the interaction of all of the factors that are addressed.

• This standard applies to general thermal comfort conditions and excludes local discomforts such as

draft, vertical thermal stratification, and radiant asymmetry.

• This standard specifies thermal environmental conditions acceptable for healthy people at atmospheric

pressure equivalent to altitudes up to 3000 m in indoor spaces designed for human occupancy for

periods not less than 15 minutes.

• This standard does not address such non-thermal environmental factors as air quality, acoustics, and

illumination; nor other physical, chemical or biological space contaminants which may affect comfort or

health.

• This standard is intended for use in design of naturally ventilated buildings and evaluation of existing

thermal environments within such buildings.

• The standard applies exclusively to indoor environments without centralised HVAC systems. Such

buildings are presumed to have operable windows which the occupants have some degree of control

over. They may have some form of heating installed, but it would be controlled by the building

occupants, either individually or in small groups.

• The standard cannot be used to decide when and where to install centralised air-conditioning. While it

may provide useful information in relation to such decisions, the standard cannot be regarded as the



sole criterion. For example, the adaptive opportunity afforded the occupants of naturally ventilated

buildings should also be borne in mind.

5.2.3. Definitions

adaptive model: A linear regression model that relates indoor design temperatures or acceptable

temperature ranges to outdoor meteorological or climatological parameters. Note that the range of

applicable outdoor climates should be restricted to that appearing on the X-axis of the adaptive model’s

graph (i.e. they should not be extrapolated beyond the range of the regression models’ X-variable).

adaptive opportunity: Buildings provide their occupants with varying degrees of adaptive opportunity or

scope to adjust the internal environment (and themselves) to achieve thermal comfort. Sealed, centrally

air-conditioned office buildings with open-plan floor layouts provide minimal adaptive opportunity, while

naturally ventilated buildings with operable windows and ceiling fans within small single- or dual-occupant

offices typically afford high degrees of adaptive opportunity.

comfort, thermal: that condition of mind which expresses satisfaction with the thermal environment; it

requires subjective evaluation. Optimum thermal comfort is assumed to correspond with a thermal

preference vote of “want no change”

environment, thermal: the characteristics of the environment which affect a person’s heat loss.

environment, acceptable thermal: an environment which at least 80% of the occupants would find

thermally acceptable.

humidity, relative (rh): the ratio of the mole fraction of water vapor present in the air to the mole fraction

of water vapor present in saturated air at the same temperature and barometric pressure; alternatively, it

equals the ratio of the partial pressure (or density) of the water vapor in the air to the saturation pressure

(or density) of water vapor at the same temperature.

mean monthly (or daily) outdoor effective temperature: Arithmetic average of 6am outdoor ET*

(assumed minimum), and 3pm outdoor ET* (assumed maximum) for a calendar month or particular day.



naturally ventilated: Those premises in which a centralised heating, ventilation and air-conditioning

systems are absent and windows are operable. Some form of heating may be present, but it would

normally be under the control of building occupants, either individually or in small groups.

neutrality, thermal: the indoor thermal index value (usually operative temperature) corresponding with a

maximum number of building occupants voting “neutral” on the thermal sensation scale.

preference, thermal: a conscious desire for change in one’s thermal state, commonly graded into the

categories, 1“want cooler,” 2 “want no change,” 3“want warmer”; it requires subjective evaluation.

Preferred temperature, i.e. that corresponding with a maximum number of “2” votes, corresponds

reasonably well with thermal neutrality in naturally ventilated buildings.

sensation, thermal: a conscious feeling commonly graded into the categories, -3 cold, -2 cool, -1 slightly

cool, 0 neutral, +1 slightly warm, +2 warm, and +3 hot; it requires subjective evaluation. Optimum

thermal comfort corresponds reasonably well with a thermal sensation vote of “neutral” in naturally

ventilated buildings.

temperature, air (ta): the dry-bulb temperature of the air surrounding the occupant.

temperature, mean radiant (tr): the uniform surface temperature of an imaginary black enclosure in which

an occupant would exchange the same amount of radiant heat as in the actual nonuniform space.



temperature, operative (to): the uniform temperature of an imaginary black enclosure in which an

occupant would exchange the same amount of heat by radiation plus convection as in the actual non-

uniform environment. Operative temperature is numerically the average of the air temperature (ta) and

mean radiant temperature (tr), weighted by their respective heat transfer coefficients (hc and hr):

th t h t

h ho

c a r r

c r

= ++

( )( )

which typically equates to the arithmetic average of mean air and radiant temperatures

temperature, effective (ET*): the operative temperature (to) of an enclosure at 50% relative humidity

which would cause the same sensible plus latent heat exchange from a person as would the actual

environment.

temperature, optimum operative: the operative temperature that satisfies the greatest possible number of

people at a given clothing and activity level. Optimum operative temperature in this standard corresponds

reasonably well with both thermal neutrality and preferred temperature.

zone, occupied: the region normally occupied by people within a space, generally considered to be

between the floor and 1.8 m above the floor and more than 0.6 m from walls or fixed air conditioning

equipment.

5.2.4. Conditions for an acceptable thermal environment.

The conditions for an acceptable thermal environment shall be based exclusively on the adaptive model

(linear regression) approach. The PMV/PPD model is inapplicable to naturally ventilated premises

because it only partially accounts for processes of thermal adaptation to indoor climate. The prescription

of summer and winter comfort zones is inappropriate for this standard because the steep gradient on the

naturally ventilated adaptive model would render climatological definitions of universal “summer” and

“winter” conditions misleading.

The adaptive models in this section can be applied where weather data in the form of mean outdoor

effective temperature for the relevant time of year are available. These need to be calculated from basic

outdoor air temperature maxima (3 pm) and minima



(6 am), along with coincident humidity. In the absence of current meteorological observations, published

mean climatological data for the relevant month from the nearest weather station may suffice.

16

18

20

22

24

26

28

30

32

5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)

com

fort

tem

p (o C

)



comfort temp. in NV = 18.9 + 0.255 * outdoor ET*

Figure 5.4: The adaptive comfort zone’s optimum and limits for an 80% acceptability level in naturally ventilated

premises.

16

18

20

22

24

26

28

30

32

5 10 15 20 25 30 35mean daily outdoor effective temperature (oC)

com

fort

tem

p (o C

) 90% acceptability upper limit


comfort temp. in NV = 18.9 + 0.255 * outdoor ET*

Figure 5.5: The adaptive comfort zone’s optimum and limits for a 90% acceptability level in naturally ventilated premises.



The charts in this standard require input of the relevant value of outdoor ET* on the X-axis and then

reading off the optimum comfort temperature, upper and lower acceptable limits on the Y-axis. Choose

either Figure 5.4 or Figure 5.5 depending on whether an 80% or 90% acceptability level is being sought.


Bibliography page 171 MRL Australia

BIBLIOGRAPHY

1. Abdulshukor. 1993. Human Thermal Comfort in the Tropical Climate.

Unpublished Ph.D. thesis. University of London, London (cited by Humphreys

1993).

2. Abro, R.S. 1994. “Recognition of passive cooling techniques.” Renewable

Energy, Vol. 5, No. II, pp.1143-1146.

3. Ang, B.W. 1986. ASEAN Energy Demand Singapore: ISEAS.

4. ASHRAE. 1981 Standard 55 - Thermal Environmental Conditions for Human

Occupancy. Atlanta: ASHRAE Inc.

5. ASHRAE. 1992. Standard 55 - Thermal Environmental Conditions for Human

Occupancy. Atlanta: ASHRAE Inc.

6. Auliciems, A. 1969. “Effects of weather on indoor thermal comfort.” International

J. of Biometerorology, Vol. 13, pp. 147-162.

7. Auliciems, A. 1981. “Towards a psychophysiological model of thermal

perception.” Int J Biometeorology, Vol .25, pp.109-122.

8. Auliciems, A. 1981b. “Global differences in indoor thermal requirements.”

Presented to the ANZAAS Symposium M2, Brisbane, 11 May 1981.

9. Auliciems, A. 1983. “Psychophysical criteria for global thermal zones of building

design.” Biometeorology, No.8, Part 2, Supplement to Vol. 26 (1982), International

Journal of Biometeorology, pp. 69-86.

10. Auliciems, A. 1986. “Air conditioning in Australia III: Thermobile controls.” Arch.

Science Review, Vol. 33, pp. 43-48.

11. Auliciems, A. 1989. “Thermal Comfort.” In Building Design and Human

Performance. Ed: Ruck, N. New York: Van Nostrand. pp.71-88.

12. Baird, G.; Brander, W.D.S.; Pool, F.; and Donn, M.R. 1981. Building energy use

and the design-user interface. In: Proceedings of Australian and New Zealand

Arch. Sci. Assoc. Conference. Ed: Szokolay, S. Canberra: ANZAScA: pp. 19-26.



13. Baker, N.V. 1993. Thermal comfort evaluation for passive cooling - A PASCOOL

task. In: Proceedings of Conference on Solar Energy in Architecture and Planning.

Florence: HS Stephens and Associates.

14. Baker, N.; and Standeven, M. 1994. “Comfort criteria for passively cooled

buildings. A PASCOOL task.” Renewable Energy, Vol. 5, No. 5-8 (Aug), pp 977-

984.

15. Ballantyne, E.R.; Hill, R.K.;and Spencer, J.W. 1977. “Probit Analysis of Thermal

Sensation Assessments.” Int. J. Biometeor, Vol. 21, No. 1, pp. 29-43.

16. Barnett, D.L.; and Browning, W.D. 1995. A Primer on Sustainable Building.

Rocky Mountain Institute.

17. Bean, W.B.; and Eichna, L.W. 1943. “Performance in relation to environmental

temperature: Reactions of normal young men to simulated desert environment.”

Federation Proceedings: Federation of American Societies of Experimental

Biology, Vol. 2, pp.144-158.

18. Berger, X. 1988. “The pumping effect of clothing.” Int. J. Ambient Energy, Vol. 9,

No. 1, pp.37-46.

19. Berglund, L.G. 1995. Comfort Criteria - Humidity and Standards. In: Proceeding of

the Pan Pacific Symposium on Building and Environmental Conditions in Asia. Ed:

Nakahara, N. Vol. 1, pp. 369-382. Nagoya U: Nagoya.

20. Berglund, L.; and McNall, P.E., Jr. 1973. “Human sweat film area and composition

during prolonged sweating.” Journal of Applied Physiology, Vol. 35, No. 5,

pp.714-718.

21. Benton, C.C.; and Brager G.S. 1994. Sunset Building: A study of occupant thermal

comfort in support of PG&E’s advanced customer technology test (ACT2) for

Maximum Energy Efficiency, Final Report, CEDR -06-94, Center for Environmental

Design Research, University of California, Berkeley.

22. Benzinger, T.H. 1979. The physiological basis for thermal comfort. In: Indoor

Climate Eds: Fanger and Valbjorn. Copenhagen: DBRS.



23. Black, F.A., and Milroy, E.A. 1966. “Experience of airconditioning in offices.” J.

Inst. Heat. Vent. Engrs, Vol. 34, pp. 188-196.

24. Bordass, W.T.; and Leaman, A.J. 1993. Control strategies for building services.

In: Proceedings of the Conference on Advanced Systems of Passive and Active

Climatisation. Commission of the European Communities Directorate-General for

Energy, Barcelona, June 3-5, 1993.

25. Brager, G.S.; Fountain, M.; Benton, C.C.; Arens, E.A.; and Bauman, F.S. 1994. “A

comparison of methods for assessing thermal sensation and acceptability in the

field,” In Thermal Comfort: Past, Present and Future. Eds: Oseland, N.A.; and

Humphreys, M.A.

26. Bruce, W. 1960. “Man and his Thermal Environment: physiological adjustments to

conditions and assessment of comfort in buildings.” Technical Paper, No. 84:

Division of Building Research. Ottawa: National Research Council.

27. Busch, 1990. “Thermal responses to the Thai office environment.” ASHRAE

Trans., Vol. 96, No. 1, pp.859-872.

28. Canter, D. 1983. “The purposive evaluation of places - a facet approach,”

Environment and Behaviour, Vol.15, No. 6, pp.659-698

29. Carlton-Foss, J.A. 1982. “Energy engineering for occupied spaces.” ASHRAE J.,

Vol .24, No. 10, pp.776-788. .

30. Cena, K.M.; Spotila, J.R.; and Avery, H.W. 1986. “Thermal comfort of the elderly is

affected by clothing, activity, and physiological adjustment.” ASHRAE

Transactions, Vol. 92, No. 2., pp. 329-342.

31. Cena, K.M. 1994. “Thermal and non-thermal aspects of comfort surveys in homes

and offices.” In: Thermal Comfort: Past, Present and Future. Eds: Oseland, N.A.

and Humphreys, M.A.

32. Chatonnet, and Cabanad, 1965. “The perception of thermal comfort.” Int. J.

Biometeorology, Vol. 9, No. 2, pp. 183-193.

33. Clark, R.P.; and Edholm, O.G. 1985. Man and his thermal environment. Edward

Arnold.



34. Cooper, I. 1982. “Comfort & Energy Conservation: a need for reconciliation.”

Energy & Buildings, Vol. 5, pp. 83-87

35. de Dear, R.J. and Auliciems, A. 1985. “Validation of the Predicted Mean Vote

model of thermal comfort in six Australian field studies.” ASHRAE Trans., Vol. 91,

No. 2, pp.452-468.

36. de Dear, R.J. 1985. Perceptual and adaptational bases for the management of

indoor climate. University of Queensland Ph.D thesis.

37. de Dear, R.J. and Auliciems, A. 1986. “Air conditioning in Australia II: User

attitudes.” Arch. Science Review, Vol. 31, pp. 19-27.

38. de Dear, R.J.; Knudsen, H.N.; and Fanger, P.O. 1989. "Impact of air humidity on

thermal comfort during step-changes”. ASHRAE Transactions, Vol.95, No. 2,

pp.336-350.

39. de Dear, R.J.; Leow, K.G.; and Ameen, A. 1991a. "Thermal comfort in the humid

tropics -- Part I: Climate chamber experiments on temperature preferences in

Singapore." ASHRAE Transactions, Vol. 97, No. 1, pp.874-879.

40. de Dear, R.J., Leow, K.G. and Ameen, A. 1991b. "Thermal comfort in the

equatorial climatic zone -- Part II: Climate chamber experiments on thermal

acceptability in Singapore ". ASHRAE Transactions, Vol. 97, No. 1, pp.880-886.

41. de Dear, R.J., Leow, K.G. and Foo, S. C. 1991c. "Thermal comfort in the humid

tropics: Field experiments in air conditioned and naturally ventilated buildings in

Singapore." International Journal of Biometeorology, Vol. 34, pp.259-265.

42. de Dear, R.J. 1994. Outdoor climatic influences on indoor thermal comfort

requirements. In: Thermal comfort: past present and future. Eds: Oseland, N.A.;

and Humphreys, M.A. UK: BRE.

43. de Dear, R.J.; Fountain, M.E.; Popovic, S.; Watkins, S.; Brager, G.; Arens, E.; and

Benton, C. 1993. A Field Study of Occupant Comfort and Office Thermal

Environments in a Hot-Humid Climate : Final Report on ASHRAE RP-702.

Sydney: MPRL.



44. de Dear, R.J. and Fountain, M.E. 1994. "Field experiments on occupant comfort

and office thermal environments in a hot-humid climate," ASHRAE Transactions,

Vol. 100, No. 2, pp.457-475.

45. de Dear, R.J. and Fountain, M.E. 1994. Cover feature -- "Thermal comfort in air-

conditioned office buildings in the tropics," Journal of the Australian Institute of

Refrigerating, Air-Conditioning and Heating, Vol. 48, No. 9, pp.14-30.

46. de Dear, R.J. 1995. Thermal comfort in air-conditioned office buildings in the

tropics. In: Standards for thermal comfort Eds: Nicol, Humphreys, Sykes and

Roaf). London: E and FN Spon, pp.122-131.

47. Donnini, G.; Molina, J.; Martello, C.; Lai, D.H.C.; Kit, L.H.; Chang, C.Y.; Laflamme,

M.; Nguyen, V.H.; and Haghighat, F. 1996. Field Study of Occupant Comfort and

Office Thermal Environments in a Cold Climate : Final Report on ASHRAE RP-

821. Montreal, Quevec, Canada: AND Inc.

48. Elder J.; and Tibbott, R.L. 1981. User Acceptance of an Energy Efficient Office

Building - A Case Study of the Norris Cotton Federal Office Building. NBS Bldg.

Ser. No.130. (National Bureau of Standards, Washington DC).

49. Fanger, P.O. (1970) Thermal Comfort. (Copenhagen: Danish Technical Press).

50. Fanger, P.O. 1972a. “Improvement of human comfort and resulting effects on

working capacity.” Biometeorology, No. II, 31-41.

51. Fanger, P.O. 1972b. “Near future prospects of the meteorological environment in

developing countries in deserts and tropical areas”. Biometeorology, Vol. 5, No. II,

supplement to International Journal of Biometeorology, Vol. 16, pp.31-41.

52. Fanger, P.O.; and Langkilde, G. 1975. “Interindividual differences in ambient

temperatures preferred by seated persons.” ASHRAE Transactions, Vol. 81, No.

2, pp.140-147

53. Fanger, P.O.; Hobjerre, J.H.; and Thomsen, J.O.B. 1977. “Can winter swimming

cause people to prefer lower room temperatures?” International Journal of

Biometeorology, Vol. 21, No. 1, pp. 44-50



54. Fanger, P.O.; and Wyon, D. 1990. “Discussion section at the end of Schiller's

paper,” ASHRAE Transactions, Vol, 96, No. 1, pp. 621-622.

55. Finney, D. J. 1971. Probit Analysis 3rd Ed. Cambridge UK: Cambridge Uni Press.

56. Fishman, D.S.; and Pimbert, S.L. 1982. “The thermal environment in offices.”

Energy and Buildings, Vol. 5, No. 2, pp. 109-116.

57. Forwood, G. 1995. What is thermal comfort in a naturally ventilated buildings?. In:

Standards for thermal comfort Eds: Nicol, Humpreys, Sykes and Roaf. London: E

and FN Spon, pp.122-131.

58. Folk, G.E. 1974. “Adaptation and heat loss: the past thirty years”. Heat loss from

animals and man: assessment and control. Proceedings of the 20th Easter

School in Agricultural Science, Univ. of Nottingham, Eds: Monteith, J.L.; and

Mount, L.E. London: Butterworths.

59. Folk, G.E. 1981. Climatic change and Acclimatization. From Bioengineering,

Thermal Physiology and Comfort Eds: Cena, K.; and Clark, J.A, pp.157-168,

Amsterdam: Elsevier.

60. Fountain, M.E. and C. Huizenga (1996) WinComf : A Windows 3.1 Thermal

Sensation Model - User’s Manual. (Berkeley: Environmental Analytics).

61. Fox, 1974. “Heat acclimatisation and the sweating response”. Heat loss from

animals and man: assessment and control. In: Proceedings of the 20th Easter



62. Frisancho, A.R. 1981. Human Adaptation. U. of Mich. Press.

63. Gagge, and Nevins, 1976. “Effect of energy conservation guidelines on comfort,

acceptability and health,” Final Report of Federal Energy Administration Contract

#CO-04-51891-00. New Haven: Pierce Lab.

64. Gerlach, 1974. Environmental design to counter thermal boredom. J. Arch. Res.,

Vol. 3, pp. 15-19.



65. Givoni, B. and Goldman, R.F. 1973. “Predicting effects of heat acclimatization on

heart rate and rectal temperature.” Journal of Applied Physiology, Vol. 35, No. 6

(Dec),.

66. Givoni, B. 1992. “Comfort, climate analysis and building design guidelines.”

Energy & Buildings. Vol. 18, No. 1, pp. 11-23.

67. Glaser, E. 1966. The physiological basis of habituation. London: O.U.P.

68. Goldman, R.F.; Green, R.B.; and Iampietro, P.F. 1965. “Tolerance of hot, wet

environments by resting men”. J. of Applied Physiology, Vol. 20, No. 2, pp.271-

277.

69. Goldsmith, 1974. “Acclimatisation to cold in man - fact or fiction?”. Heat loss from

animals and man: assessment and control. In: Proceedings of the 20th Easter



70. Gonzalez, R.R.; Pandolf, K.B.; and Gagge, A.P. 1974. “Heat acclimation and

decline in sweating during humidity transients.” J Appl Physiol. Vol. 36, No. 4,

pp.419-425.

71. Gonzalez, R.R. 1979. Role of natural acclimatization (cold and heat) and

temperature: effect on health and acceptability in the built environment. Indoor

Climate Eds: Fanger, P.O.; and Valbørn, O., pp. 737-751. Copenhagen: Danish

Building Research Institute.

72. Griffiths, I.D.; Huber, J.W.; and Baillie, A.P. 1988. The scope for energy conserving

action : A comparison of the attitudinal and thermal comfort approaches..

Environmental social psychology. NATO ASI series. Series D, Behavioural and

social sciences, No. 45. Eds: Canter, D.; Jesuino, J.C.; Soczka, L.; and

Stephenson, G.M. pp. 46-56. Dordrecht, Netherlands: Kluwer Academic

Publishers, xii, 330 pp.

73. Hardy, J.D. 1961. “Physiology of temperature regulations.” Physiol. Rev., Vol. 41,

pp. 521-606



74. Hawkes, D. 1982. “The theoretical basis of comfort in the ‘selective’ control of

environments”. Energy and Buildings, Vol. 5, pp. 127-134.

75. Heijs, W.; and Stringer, P. 1988. “Research on residential thermal comfort: some

contributions from environmental psychology.” J. Environ. Psychol., Vol. 8, pp.235-

247.

76. Helson, H. 1964. Adaptation-Level Theory. New York: Harper & Row.

77. Helson, H. 1971. Adaptation-level theory: 1970 and after. In: Adaptation-level

theory, Ed: Appley M.H. New York, Academic Press, pp. 5-17.

78. HOK. 1994. Alternative Officing. The HOK Facilities Consulting Report, No. 1,

San Francisco.

79. Humphreys, M.A. 1975. “Field studies of thermal comfort compared and applied.”

U.K. Department of Environmental Building Research Establishment Current

Paper. (76/75)

80. Humphreys, M.A. 1976. “Field studies of thermal comfort compared and applied.”

Building Services Engineer, Vol. 44, pp. 5-27.

81. Humphreys, M.A. 1978. “Outdoor temperatures and comfort indoors.” Building

Research and Practice Vol. 6, No. 2.

82. Humphreys, M.A. 1979. The influence of season and ambient temperature on

human clothing behaviour. In: Indoor Climate. Eds: Fanger and Valbjorn,

Copenhagen: DBRS.

83. Humphreys, M.A. 1981. “The dependence of comfortable temperatures upon

indoor air and outdoor climates.” Bioengineering, Thermal Physiology and

Comfort Eds: Cena, K.; and Clark, J.A., pp.229-250, Amsterdam: Elsevier.

84. Humphreys, M.A. 1994a. Field Studies and climate chamber experiments in

thermal comfort research. In: Thermal comfort: past present and future. Eds:

Oseland, N.A. and Humphreys, M.A. UK: BRE.

85. Humphreys, M A. 1994b. “An adaptive approach to the thermal comfort of office

workers in North West Pakistan.” Renewable Energy. Vol. 5, No. (ii), pp.985-992.

86. Humphreys, M.A. 1993. personal communication, BRE meeting UK.



87. Humpreys, M.A. 1995. Thermal comfort temperatures and the habits of Hobbits.

Standards for thermal comfort Eds: Nicol, Humpreys, Sykes and Roaf). London:

E and FN Spon.

88. Humphreys, M.A.; and Nicol, J.F. 1995. An adaptive guideline for UK office

temperatures. Standards for thermal comfort Eds: Nicol, Humpreys, Sykes and

Roaf. London: E and FN Spon.

89. Hunt, D.G.R.; and Gidman, M.I. 1982. “A national field survey of house

temperatures,” Building and Environment, Vol. 17, pp.107-124

90. ISO. 1984. International Standard 7730 : Moderate Thermal Environments -

Determination of the PMV and PPD Indices and Specification of the conditions of

Thermal Comfort. 1st ed. (ISO: Geneva).

91. ISO. 1994. International Standard 7730 : Moderate Thermal Environments -

Determination of the PMV and PPD Indices and Specification of the conditions of

Thermal Comfort. 2nd ed. (ISO: Geneva).

92. Ittelson, W.H. 1973. Environment perception and contemporary perception theory.

In: Environment and cognition, Ed: Ittelson, W.H. New York: Seminar Press, pp. 1-

19.

93. Kaplan, S.; and Kaplan, R. 1982. Cognition and Environment: Functioning in a

Uncertain World, New York: Praeger.

94. Kempton, W.; and Lutzenhiser, L. (Eds) 1992. Energy and Buildings. V.18.

95. Kleinbaum, D. G., L. L. Kupper and K. E. Muller. 1988. Applied Regression

Analysis and Other Multivariable Methods. 2nd Edition. California: Duxbury Press.

96. Leaman, A.; and Bordass, B. 1993. ”Building design, complexity and

manageability,” Facilities, (Sept).

97. Lovins, A.B. 1992. Air conditioning comfort: behavioural and cultural issues.

Colorado: E Source.

98. Macfarlane, W.V. 1961. “Physiological basis for air conditioning.” Architectural

Science Review, Vol 4, pp. 124-131.



99. Macfarlane, W.V. 1978. “Thermal comfort studies since 1958”. Architectural

Science Review, Vol. 21, No.4 (Dec), pp.86-92.

100. McCullough, E.; and Olesen, B.W. 1994. “Thermal insulation of chairs,” ASHRAE

Transactions, Vol.100, No. 1, pp.795-802.

101. McIntyre, D.A. 1978a. “Seven point scales of warmth,” Building Services Engineer.

Vol. 45, pp.215-226.

102. McIntyre, D.A. 1978b. “Three approaches to thermal comfort,” ASHRAE

Transactions. Vol. 84, No.1, pp.101-109.

103. McIntyre, D.A. 1980. “Design requirements for a comfortable environment”,

Bioengineering: Thermal Physiology and Comfort Eds: Cena, K.; and Clark, J.A.,

Amsterdam: Elsevier. pp.157-168.

104. McIntyre, D.A. 1982. “Chamber studies - reductio ad absurdum?”, Energy and

Buildings, Vol. 5, pp. 89-96.

105. Milne G.R. 1995. The energy implications of a climate-based indoor air

temperature standard. Standards for thermal comfort Eds: Nicol, Humpreys,

Sykes and Roaf. London: E and FN Spon. pp.182-189.

106. National Climate Data Centre - NOAA. 1992. International Station Meteorology

and Climatology Summary - CDROM, Vol 2.

107. Nicol, J.F. and Humphreys, M.A. 1972. Thermal comfort as part of a self regulating

system. Proceedings of CIB Commission W45 Symposium - Thermal comfort and

moderate heat stress. Eds: Langdon, Humphreys and Nicol, Building Research

Establishment Report. London: Her Majesty’s Stationery Office (HMSO), pp. 263-

274.

108. Nicol, J.F. 1974. “An analysis of some observations of thermal comfort in

Roorkee, India and Baghdad, Iraq.” Annals of Human Biology, Vol. 1, No. 4,

pp.411-426.

109. Nicol, F. 1993. Thermal Comfort - A Handbook for Field Studies Toward an

Adaptive Model. UK: University of East London.



110. Nicol, J.F.; Jamy G.N.; Sykes O.; Humpreys, M.A.; Roaf, S.; and Hancock, M.

1994. A survey of comfort temperatures in Pakistan: towards new indoor

temperature standards. Oxford: Oxford Brookes Univ. (Report).

111. Nicol, J.F. and Raja, I.A. 1996. Thermal comfort, time and posture: exploratory

studies in the nature of adaptive thermal comfort. School of Architecture, England:

Oxford Brookes University.

112. Oseland, N A. 1994a. “Comparison of the predicted and reported thermal

sensation vote in homes during winter and summer.” Energy & Buildings. Vol. 21,

No. 1, pp. 45-54.

113. Oseland, N.A. 1994b. A within-groups comparison of predicted and reported

thermal sensation votes in climate chambers, offices and homes. Healthy

Buildings, Vol 1. pp. 41-46. Eds: Bánhidi et. al. Budapest: Technical Univ. of

Budapest, Also accepted for publication in Energy and Buildings.

114. Oseland, N.A. 1995. “Predicted and reported thermal sensation in climate

chambers, offices and homes.” Energy and Buildings Vol.22, pp. (in press).

115. Paciuk, M. 1989. The role of personal control of the environment in thermal comfort

and satisfaction at the workplace. Doctoral dissertation, University of Wisconsin,

Milwaukee.

116. Paciuk, M. 1990. The role of personal control of the environment in thermal comfort

and satisfaction at the workplace. Coming of age. EDRA 21/1990. Eds: Selby,

R.I.; Anthony, K. H.; Choi, J.; Orland, B., pp. 303-312. Oklahoma City, OK, US:

Environmental Design Research Association, xviii, 372 pp.

117. Parsons, K. 1994. “Thermal comfort standards: Past, present and future”, and

open discussion that follows. In: Thermal Comfort: Past, Present and Future, Eds:

Oseland, N.A.; and Humphreys, M.A. Garston, UK: BRE, pp.184-197.

118. Pimbert, S.L.; and Fishman, D.S. 1981. “Some recent research into home

heating.” J. Consumer Stud. Home Econ., Vol. 5, pp.1-12.

119. Prosser, C.L. (Ed.), 1958. Physiological Adaptation. Washington, D.C.: Am.

Physiol. Soc.



120. Ring, J.W.; and de Dear, R.J. 1991. “Temperature transients: a model for heat

diffusion through skin, thermoreceptor response and thermal sensation.” Indoor Air

- International Journal of Indoor Air Quality and Climate, Vol. 1, No. 4, pp. 448-456.

121. Rohles, F.H.; Hayter, R.B.; and Berglund, L.G. 1977. “Comfort and cold and warm

discomfort during summer and winter in northern and southern United States.”

ASHRAE Transactions, Vol. 83, Part 1.

122. Rowe, D.; Lambert, S.G.; and Wilke, S.E. 1995. “Pale green, simple and user

friendly: Occupant perceptions of thermal comfort in office buildings. In:

Standards for thermal comfort Eds: Nicol, Humphreys, Sykes and Roaf. London:

E and FN Spon, pp.59-69.

123. Russell J.A.; and Ward, L.M. 1982. “Environmental psychology.” Annual. Rev.

Psychol. Vol. 33, pp. 651-688.

124. Schiller, G.; Arens, E.; Bauman, F.; Benton, C.; Fountain, M.; Doherty, T. 1988a, "A

Field Study of Thermal Environments and Comfort in Office Buildings", ASHRAE

Transactions, Vol. 94, Part 2.

125. Schiller, G.E.; Arens, E.; Bauman, F.; Benton, C.; Fountain, M.; and Doherty, T.

1988b. A Field Study of Thermal Environments and Comfort in Office Buildings:

Final Report--ASHRAE 462. UC, Berkeley: CEDR

126. Schiller, G.E. 1990. “A comparison of measured and predicted comfort in office

buildings.” ASHRAE Transactions, Vol. 96, No. 1.

127. Sprague, C.H.; and Munson, D.M. 1974. “ A composite ensemble method for

estimating thermal insulating values of clothing,” ASHRAE Trans., Vol. 80, No. 1,

pp.120-129.

128. Veitch, R.; and Arkkelin, D. 1995. Environmental psychology - an interdisciplinary

perspective. Englewood Cliffs NJ: Prentice Hall.

129. Webb, C.G. 1959. “An analysis of some observations of thermal comfort in an

equatorial climate.” Br. J. Indust. Med, Vol. 16, pp. 297-310.

130. Williams, R.N. 1995. “Field investigation of thermal comfort, environmental

satisfaction and perceived control levels in UK office buildings.” Healthy Buildings.



131. Williamson, T.J.; Coldicutt, S.; and Penny, R.E.C. 1991. “Aspects of thermal

preferences in a hot humid climate, with particular reference to Darwin, Australia.”

Int J. of Biometeorology. Vol. 34, pp. 251-258

132. Williamson, T.J.; Coldicutt, S.; and Riordan, P. 1995. Comfort, Preferences or

Design Data? In: Standards for thermal comfort Eds: Nicol, Humphreys, Sykes

and Roaf). London: E and FN Spon, pp.50-58

133. Willis, S.; and Perera, E. 1995. “Keeping control of comfort.” Building Services,

Feb ‘95.

134. Wohlwill, J.F. 1974. “Human adaptation to levels of environmental stimulation.”

Human Ecology, Vol. 2, pp. 127-147.

135. Wyndham, C.H. 1970 “Adaptation to heat and cold.” In: Physiology, Environment

and Man, Eds: Lee, D.H.K. and Minard, D. New York: Academic Press, pp.177-

204.


Appendix A page 185 MRL Australia

APPENDIX A - THERMAL SENSATION AND NEUTRALITY FOR EACH BUILDING IN THE RP-884 DATABASE



this plot had only one value

South Wales UK (summer), HVAC building #2

-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5

Operative Temp (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5 26.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 16 17 18 19 20 21

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 16 17 18 19 20 21

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 16 17 18 19 20 21

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 16 17 18 19 20 21

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV A

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

South Wales UK (summer), HVAC

building #2

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv



South Wales UK (winter), HVAC building #7

-3

-2

-1

0

1

2

3

18 19 20 21 22


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

11 12 13 14 15 16 17

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

11 12 13 14 15 16 17

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

11 12 13 14 15 16 17

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

11 12 13 14 15 16 17

ET* (degC)

Mea

n V

ote

ashraepmv

South Wales UK (winter), HVACbuilding #1

-3

-2

-1

0

1

2

3

18 19 20 21 22


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

South Wales UK (winter), HVAC building

#1

-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv



Bangkok Thailand (summer), HVAV building #1

-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv

Bangkok Thailand (summer), HVAV building #2

-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv

Bangkok Thailand (summer), HVAC building #1

-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5 31.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5 31.5

SET (degC)

Mea

n V

ote

ashraepmv

Bangkok Thailand (summer), NV building #3

-3

-2

-1

0

1

2

3

25 26 27 28 29 30 31 32 33 34 35

Operative Temp (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

25 26 27 28 29 30 31 32 33 34


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 26 27 28 29 30 31 32


Mea

n V

ote

ashraepmv

Bangkok Thailand (summer), NV building 3

-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv

Bangkok Thailand (summer), NV

building #4

-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HBangkok Thailand (summer), NV

building #3

-3

-2

-1

0

1

2

3

24 26 28 30 32 34 36

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

24 26 28 30 32 34 36

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

24 26 28 30 32 34 36

SET (degC)

Mea

n V

ote

ashraepmv

Antioch CA US (winter), HVAC building #1

-3

-2

-1

0

1

2

3

22.5 23.5 24.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21.5 22.5 23.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5

SET (degC)

Mea

n V

ote

ashraepmv

Jakarta Indonesia (summer), HVAC building #2

-3

-2

-1

0

1

2

3

22 24 26 28 30 32


aen

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32


Mae

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32


Mae

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32


Mae

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32


Mae

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32 34

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32 34

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32 34

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

22 24 26 28 30 32 34

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 24 26 28 30 32 34

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Jakarta Indonesia (summer), NV building #1

-3

-2

-1

0

1

2

3

28 29 30 31 32 33


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

30 31 32 33 34 35

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Jakarta Indonesia (summer), Mixed building #6

-3

-2

-1

0

1

2

3

26 27 28 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

26 27 28 29 30 31

ET* (degC)

Mea

n V

ote

ashraepmv

The Jakarta, Indonesian filesdo not have SET as a variable


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Montreal Canada RP-821 (summer),HVAC building #1

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv

Montreal Canada RP-821 (summer), HVAC building #2

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27


ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Montreal Canada RP-821 (summer),

HVAC building #8

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HMontreal Canada RP-821 (summer),

HVAC building #1

-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv

Montreal Canada RP-821 (winter),HVAC building #1

-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Montreal Canada RP-821 (winter),

HVAC building #10

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)M

ean

Vot

e

ashraepmv

Brisbane Australia (summer), HVAC building #1

-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31

SET (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv

Brisbane Australia (summer), NV building #1

-3

-2

-1

0

1

2

3

25.5 26.5 27.5 28.5 29.5 30.5 31.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25.5 26.5 27.5 28.5 29.5 30.5 31.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25.5 26.5 27.5 28.5 29.5 30.5 31.5


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

25.5 26.5 27.5 28.5 29.5 30.5 31.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25.5 26.5 27.5 28.5 29.5 30.5 31.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv

Darwin Australia (summer-dry), HVAC building #1

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32


ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv

Darwin Australia (summer-dry), HVAC

building #4

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv

Darwin Australia (summer-wet), HVAC building #8

-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv

Darwin Australia (Summer), HVAC building #14

-3

-2

-1

0

1

2

3

20 22 24 26 28 30

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HDarwin Australia (summer-wet), HVAC

building #10

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv

Darwin Australia (summer-wet), HVAC

building #14

-3

-2

-1

0

1

2

3

21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv

Melbourne Australia (summer), HVAC building #1

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5 29.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv

Melbourne Australia (summer), HVAC

building #4

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv

Melbourne Australia (summer), NV building #1

-3

-2

-1

0

1

2

3

10 15 20 25 30 35


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

12 14 16 18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

12 14 16 18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

12 14 16 18 20 22 24 26 28 30 32

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)M

ean

Vot

e

ashraepmv

Ottawa Canada (summer), HVAC building #1

-3

-2

-1

0

1

2

3

18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)M

ean

Vot

e

ashraepmv

Karachi Pakistan (summer), NV building #1

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35


Mea

n V

ote

ashraepmv

Karachi Pakistan (winter), NV building #1

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35


Mea

n V

ote

ashraepmv

Multan Pakistan (summer), NV building #2

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Peshawar Pakistan (summer), NV

building #3

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Peshawar Pakistan (winter), NV building #3

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Quetta Pakistan (summer), NV building #4

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Quetta Pakistan (winter), NV building #4

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv



Saidu Sharif Pakistan (summer), NV building #5

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Saidu Sharif Pakistan (winter), NV building #5

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv

Peshawar Pakistan (summer), NV building #3

-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Karachi Pakistan (winter), NV

building #1

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HQuetta Pakistan (winter), NV

building #4

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv

Athens Greece (summer), NV building #1

-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv

Athens Greece (summer), NV building 5

-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 20 25 30 35 40

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Athens Greece (summer), NVbuilding #2

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Athens Greece (summer), NV

building #4

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

10 15 20 25 30 35 40

SET (degC)

Mea

n V

ote

ashraepmv

Oxford UK (summer), NV building #1

-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28 30


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

14 16 18 20 22 24 26 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

15 17 19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

15 17 19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

15 17 19 21 23 25 27 29 31 33

SET (degC)

Mea

n V

ote

ashraepmv

Sydney Australia (summer), Mixed building #1

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv

Sydney Australia (winter), Mixed building #1

-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)M

ean

Vot

e

ashraepmv

Sydney Australia (winter), HVAC building #2

-3

-2

-1

0

1

2

3

21 22 23 24


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Sydney Australia (winter), HVAC

building #2

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)

Mea

n V

ote

ashraepmv

San Francisco Bay Area RP-462 (summer) HVAC building #2

-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv

San Francisco Bay Area RP-462 (summer), HVAC building #3

-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 21.5 22.5 23.5 24.5 25.5 26.5 27.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HSan Francisco Bay Area RP-462 (summer), HVAC building #10

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5 28.5 30.5 32.5

SET (degC)M

ean

Vot

e

ashraepmv

San Francisco Bay Area RP-462 (summer), NV building #1

-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5


Mea

n V

ote

ashraepmv

San Francisco Bay Area RP-462

(summer), NV building #1

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HSan Francisco Bay Area RP-462

(summer), NV building #6

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20.5 22.5 24.5 26.5 28.5 30.5

SET (degC)

Mea

n V

ote

ashraepmv

San Francisco Area RP-462 (winter), HVAC building #2

-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29


Mea

n V

ote

ashraepmv

San Francisco Bay Area RP-462 (winter),

HVAC building #2

-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv

San Francisco Bay Area RP-462 (winter), HVAC building #3

-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


HVAC building #4

-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv



San Francisco Bay Area RP-462 (winter), NV building #1

-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 20.5 21.5 22.5 23.5 24.5 25.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28 29

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28 29

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26 28 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26 27 28 29

SET (degC)

Mea

n V

ote

ashraepmv

Townsville Australia RP-702 (summer-dry), HVAC building #1

-3

-2

-1

0

1

2

3

20 22 24 26

Operative Temperature (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

Operative Temperature (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv

Townsville Australia RP-884

(summer-dry), HVAC building #4

-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24 25 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HTownsville Australia RP-702


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv



-3

-2

-1

0

1

2

3

20 22 24 26 28 30 32

SET (degC)

Mea

n V

ote

ashraepmv

Townsville Australia RP-702 (summer-wet), HVAC building #1

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)M

ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


(summer-wet), HVAC building #6

-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMVA

SH


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24 25 26 27 28 29 30

SET (degC)M

ean

Vot

e

ashraepmv

Merseyside UK (summer), NV building #1

-3

-2

-1

0

1

2

3

16 17 18 19 20 21 22 23 24 25 26


Mea

n V

ote

pmv


-3

-2

-1

0

1

2

3

16 17 18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

16 17 18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv

no ash versus pmv graph for building 1, Merseyside UK.


-3

-2

-1

0

1

2

3

16 18 20 22 24 26

ET* (degC)

Mea

n V

ote

pmv


-3

-2

-1

0

1

2

3

16 18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

16 18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HMerseyside UK (summer), NV

building #3

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

pmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv

Merseyside UK (winter), NV building #3

-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26


ean

Vot

e

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv

Merseyside UK (winter), NV

building #6

-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

HMerseyside UK (winter), NV

building #6

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 19 20 21 22 23 24 25 26 27

SET (degC)M

ean

Vot

e

ashraepmv

Merseyside UK (winter), Mixed building #4

-3

-2

-1

0

1

2

3

18.5 20.5 22.5 24.5 26.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

18 20 22 24 26

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H

Merseyside UK (winter), Mixed building 4

-3

-2

-1

0

1

2

3

19 20 21 22 23 24 25 26 27

SET (degC)

Mea

n V

ote

ashraepmv

Singapore (summer), HVAC building #1

-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31 33 35

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H




-3

-2

-1

0

1

2

3

17 19 21 23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv

Singapore (summer), NV building #2

-3

-2

-1

0

1

2

3

26 27 28 29 30 31 32


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

27 29 31 33 35 37

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

23 25 27 29 31 33 35 37

SET (degC)

Mea

n V

ote

ashraepmv

Grand Rapids Michigan US (winter), HVAC building #1

-3

-2

-1

0

1

2

3

22.5 23.5 24.5


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

22 23 24

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

23 24 25 26 27 28

SET (degC)M

ean

Vot

e

ashraepmv

San Ramon CA US (summer), HVAC building #3

-3

-2

-1

0

1

2

3

21 22 23 24


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

21 22 23 24

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

21.5 22.5 23.5 24.5 25.5 26.5 27.5

SET (degC)

Mea

n V

ote

ashraepmv

San Ramon CA US (winter), HVAC building #1

-3

-2

-1

0

1

2

3

19 20 21 22 23 24


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19 20 21 22 23 24

ET* (degC)

Mea

n V

ote

ashraepmv



San Ramon US (winter), HVAC building #2

-3

-2

-1

0

1

2

3

19 20 21 22 23 24

ET* (degC)

Mea

n V

ote

ashraepmv

San Ramon CA US (winter), HVACbuilding #1

-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5

SET (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

19.5 21.5 23.5 25.5 27.5

SET (degC)

Mea

n V

ote

ashraepmv

Auburn CA US (winter), HVAC building #1

-3

-2

-1

0

1

2

3

20 21 22 23 24


Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

20 21 22 23 24

ET* (degC)

Mea

n V

ote

ashraepmv


-3

-2

-1

0

1

2

3

-3 -2 -1 0 1 2 3

PMV

AS

H


-3

-2

-1

0

1

2

3

19 21 23 25 27 29 31

SET (degC)M

ean

Vot

e

ashraepmv


Appendix B page 227 MRL Australia

APPENDIX B - PREFERRED TEMPERATURE FOR EACH BUILDING IN THE RP-884 DATABASE




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36

indoor operative temperature (oC)

perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)San Francisco Bay Area RP-462 (winter),

NV building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


HVAC building #2

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

San Francisco Bay Area RP-462(winter), HVAC building #4

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

San Francisco Bay Area RP-462(winter), HVAC building #9

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)Brisbane Australia (summer),

HVAC building #3

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Brisbane Australia (summer),

NV building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)Darwin Australia (summer-wet),

HVAC building #12

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Melbourne Australia (summer),

HVAC building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)Townsville Australia RP-702


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


(summer-wet), HVAC building #4

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

) Athens Greece (summer), NV

building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Athens Greece (summer), NV

building #5

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Grand Rapids Michigan (winter), HVAC building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)Montreal Canada RP-821

(summer), HVAC building #9

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Montreal Canada RP-821

(summer), HVAC building #12

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)

Montreal Canada RP-821 (winter), HVAC building #1

0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)




0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


0

20

40

60

80

100

16 18 20 22 24 26 28 30 32 34 36


perc

enta

ge w

antin

g ch

ange

(%

)


Appendix C page 235 MRL Australia

APPENDIX C - SUMMARY OF THE ORIGINAL FIELD EXPERIMENTS COMPRISING THE ASHRAE RP-884 DATABASE



C.1. Project Title - ASHRAE TC 2.1 sponsored RP-702

Project filenames in the RP-884 database

This project is disseminated as file numbers 36 (summer “dry” - HVAC) and 37 (summer

“wet” - HVAC) in the RP-884 database.

Project researchers and class of investigation

Richard de Dear (Macquarie University, Sydney Australia) and Marc Fountain (University of

California at Berkeley, USA).

A CLASS-1 field experiment sponsored by ASHRAE TC 2.1.

Project publications

de Dear, R.J. and M.E. Fountain (1994) "Field experiments on occupant comfort and office

thermal environments in a hot-humid climate," ASHRAE Transactions, Vol.100(2), pp.457-

475.

de Dear, R.J. and M.E. Fountain (1994) Cover feature -- "Thermal comfort in air-conditioned

office buildings in the tropics," Journal of the Australian Institute of Refrigerating, Air-

Conditioning and Heating, Vol.48(9), pp.14-30.

de Dear, R.J., M.E. Fountain, S. Popovic, S. Watkins, G. Brager, E.Arens and C Benton

(1993) A Field Study of Occupant Comfort and Office Thermal Environments in a Hot-Humid

Climate : Final Report on ASHRAE RP-702. (MRL: Sydney), 162 pp.

Project location, climate and season

The project was located in Townsville on the north-eastern coast of Australia which falls

within a Tropical Savanna climate zone (wet-dry tropics). One field experiment conducted in

the “Dry” season (warm-dry “summer”), another experiment conducted in the “wet” season

(hot-wet “summer”).



Sample buildings

Twelve buildings, all offices, were studied.

Building Code

(blcode)

Sample Size (n)

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

1 56 VAV 2,010m2 3 storeys, mainly open plan.

Federal Government department.

2 22 Central AC (CAV)

3,944 m2 4 levels, private and mult-occupant.offices.

Tertiary education administration.

3 61 VAV 17,820 m2 12 storeys mainly open plan.

multi-tenant office tower.

4 22 CAV 4,865 m2 Twin tower design, mainly open plan.

Government departments

5 45 CAV 1,860 m2 5 storeys, mainly open plan.

Single tenant - power utility administration.


Regional bank headquarters.


Local government offices.


State government offices.


State government offices.

10 63 VAV 2,076 m2 3 storeys, mainly open plan.

Office building.


Insurance and legal firms.


Government department.

Instruments

Class-1 instrumentation includes three heights above floor level. Anemometry was

measured by DANTEC 54R10 omnidirectional heated elements with fast time-constant for

turbulence intensity calculations. Air temperature was measured by YSI series 700 probes

(thermistors) and globe temperatures measured by fixing a table tennis ball (40mm diam.)

over the sensor with appropriate steps taken to achieve correct emissivity. Dewpoint

temperature (humidity) measured by a General Eastern DEW-10 chilled-mirror transducer.



Radiant asymmetry was measured by a Bruel and Kjaer plane radiant asymmetry sensor

(MM 0036).

Questionnaire

The questionnaire was divided into two parts, background and on-line surveys. The

background questionnaire covered demographics, contextual and psychological factors. The

on-line questionnaire covered the subjects assessment of their immediate thermal

environment, such as their thermal sensation on a 7-point scale, acceptability as a yes/no

response, thermal preference on a 3-point scale, current garment insulation assessed by

tables and algorithms in ASHRAE Standard 55-1992 and metabolic activity assessed by

ASHRAE Standard 55-92 and ISO 7730. Metabolic activity was recorded at four distinct

time periods, from which an overall metabolic rate was established. The on-line

questionnaire was conducted at the same time as physical measurements were being made

of the subjects environment.

Outdoor meteorological data

Concurrent three-hourly observations from Townsville Airport (purchased from Australian

Bureau of Meteorology), from which air temperature and relative humidity at 600 hours and

1500 hours was extracted for RP-884 purposes.

RP-884 standardization assumptions

The design of the database structure and coding conventions throughout the ASHRAE

Adaptive Model Project (RP-884) was based on de Dear and Fountains’ (1994) Townsville

(RP-702) project.



C.2. Project Title - Thermal comfort studies in modern industrial buildings.

Project file names in the RP-884 database

This project is disseminated as file numbers 1 (summer - HVAC) and 2 (winter - HVAC) in

the RP-884 database.


Jill C. Brown (Ph.D thesis, University of Wales, Cardiff). This is a CLASS-2 field

experiment.


Brown, J. C. (1995). Thermal Comfort Studies in Modern Industrial Buildings, Ph.D. Thesis,

University of Wales, Cardiff.

Brown, J. C. and Jones, P. J. (1993). Thermal Comfort in Modern Industrial Buildings, Clima

2000 Conference, London, Organised by the Chartered Institute of Building Services

Engineers.


This project was conducted in South Wales, UK. More precisely, in Cwmbran, Gwent;

Newport, Gwent; Ebbw Vale, Gwent; Maesteg, Mid Glamorgan; Cwmfelinfach, Gwent;

Gwent; Llanelli, Dyfed and Llantrisant, Glamorgan. Summer and Winter seasons

investigated. Climatically, this region can be classified as west coast marine.

Instruments

Indoor climatic instrumentation included: pre-calibrated thermistors to measure air

temperature, hot-wire anemometer for air speed, solid-state hygrometer to measure

humidity, and a thermistor inside a 38mm diameter ping-pong ball to measure globe

temperature. Air temperature was measured at ankle, waist and head heights (0.3m, 1.5m

and 2m) while all other parameters were only measured at waist height.

Questionnaire

The questionnaire addressed both conditions at the time of physical measurements and

typical/overall conditions, of which only the former was used for RP-884’s purposes.



Sensation was rated on the ASHRAE 7-pt scale. The questionnaire assessed thermal

preference but not thermal acceptability. Metabolic ratings were established at the time of

the questionnaire and prior to questionnaire, using the ASHRAE 55-92 standard for

guidance. However, the author expressed reservations that this checklist did not fully

describe the types of activities being performed within the study. Clo was estimated using

the ASHRAE 55-92 and ISO/DIS 9920-91 checklist and if clothing insulation data was

absent then an estimation was made using the garment weight relationship suggested by

McCullough et al., 1984).

Sample buildings

Location Building Code

(blcode)

Sample Size (n)

and season

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

Cwmbran, Gwent

1 16 - winter HVAC 576m2 Light Industrial Factory

Newport, Gwent 2 17 - summer HVAC 3000m2 Med-heavy Industrial Factory

Ebbw Vale, Gwent

3 15 - summer HVAC 1000m2 Light-med Industrial Factory

Maesteg, Glamorgan

4 32 - summer HVAC c. 1500m2

Light Industrial Factory

Llanelli, Dyfed 5 6 - winter HVAC c. 850m2


Llantrisant, mid - Glamorgan

6 9 - winter HVAC c. 2500m2


Llantrisant, mid - Glamorgan

7 7 - winter HVAC c. 2500m2

Med-heavy Industrial Factory

Cwmfelinfach, Gwent

8 16 - summer HVAC c. 6700m2

Med-heavy Industrial Factory


In the absence of accessible outdoor meteorological observations at the same time as the

questionnaire data, RP-884 researchers substituted climatological data at 600 hrs and 1500

hrs. This data was retrieved from two sources -- air temperature from the journal Weather

(using the UK Met Office site of Roose), and humidity (by derivation of dew point using the



UK Met Office site for Cardiff) based on data entries the International Station Meteorological

and Climate Summary (ISMCS 1992) CDROM.


Instrumentation in the original data set took measurements at heights 2m, 1.5m and 0.3m.

We mapped 0.3m to 0.1m and 1.5m to 1.1m for the RP-884 database. Clo was estimated

with the ASHRAE 55-92 checklist so no corrections were needed, but the activity variable in

the original data set had to be used to determine whether or not the subject was seated and

so whether 0.15 clo for the insulation due to a chair needed to be subtracted. This provided

two variables within the RP-884 database, clothing insulation with and without the effects of

a chair. The research design was cross-sectional which satisfied the assumptions for RP-

884, that all subjects were independent.



C.3. Project Title - Doctoral dissertation. From comfort to kilowatts: an integrated

assessment of electricity conservation in Thailand’s commercial sector.


This project is disseminated as file numbers 3 (summer - HVAC) and 4 (summer - NV) in the

RP-884 database.


John F. Busch, Jr (Lawrence Berkeley Lab. Berkeley California, USA).

This is a CLASS-2 field experiment.


Busch, 1990 “Thermal responses to the Thai office environment.” ASHRAE Trans., V. 96(1),

pp. 859-872.

Busch J. F. (1992) A tale of two populations: thermal comfort in air-conditioned and naturally

ventilated offices in Thailand. Energy and Buildings Vol 18 pp 235-249.

Busch J. (1995) Thermal comfort in Thai air-conditioned and naturally ventilated offices in

Thailand Standards for thermal comfort pp 114-121.

Busch J. F. (1990) From Comfort to Kilowatts - An Integrated Assessment of Electricity

Conservation in Thailand’s Commercial Sector. (UC Berkeley PhD. Thesis).


The project was located in Bangkok, Thailand (peninsular, Southeast Asia). Bangkok is the

largest city in Thailand as well as being the capital. Being tropical, Bangkok does not display

much seasonality and can been classified under a hot humid climate. The project was

conducted in the hot season and the wet season.



Sample buildings

Building Code (blcode)

Sample Size (n)

Climate Controls (bldgtype)

Floor Area

Occupancy Pattern

1 380 HVAC Sathorn Thani (SH and SW)

2 389 HVAC Thai Farmers Bank (TH and TW)

3 194 NV Dept. Science Services (DH and DW)

4 173 NV Ministry of ST and E (MH and MW)

5 25 NV KMIT Instit. Tech (pilot) (PH and PW)

Instruments

The instrumentation was packaged into a “toolbox” which was placed in the subject’s

occupied zone, typically at a height of 0.6m above floor, or on their desk. Air and globe

temperatures were registered with calibrated thermistors. The globe thermometer was

based on a 38mm ping pong ball. Air speeds were registered with a Kurz 403 “hot-film”

anemometer in the vicinity of the subject. Humidity was recorded with a steady-state device.

All sensors were connected to a Campbell Scientific CR21 datalogger which was dumped

into a tape recorder at the end of every day in the field.

Questionnaire

Subjects who had been seated at their workstations for more than 15 minutes were eligible

for inclusion in the sample. The questionnaire covered basic sensation and preference

items. Metabolic and clothing scales/check-lists were based on the McIntyre (1980) tables.


Outdoor meteorological data were collected by the original researcher from the Royal Thai

Meteorological Department. Daily maxima and minima for temperature and humidity were

extracted for the RP-884 database.


For this study clo was estimated by the McIntrye (1980) method. Clo therefore required

correction to the ASHRAE 55-92 Standard for RP-884 purposes. To this 0.15 clo was

added to create a separate variable accounting for the clothing ensemble and insulation



effects of a chair. The research design of this project was cross-sectional which satisfied the

assumptions for RP-884, that all subjects were independent.



C.4. Project Title - The CSAA, Antioch (1995) component of the Advanced Customer

Technology Test (ACT2) project.


Charles C. Benton and Gail S. Brager (CEDR, Department of Architecture, University of

California, Berkeley). This is a CLASS-1 field experiment.


This project is disseminated as file number 5 in the RP-884 database.


Benton, C. et al. Advanced Customer Technology Test (ACT2) CSAA Progress Report.

(CEDR UC Berkeley).

Brager, G et al. (1994) “A comparison of methods for assessing thermal sensation and

acceptability in the field,” In Thermal Comfort: Past, Present and Future. (eds N. A. Oseland

and M. A. Humphreys).


The ACT2 project was based on the ASHRAE RP-702 project (the hot-humid field

experiment in Townsville Australia, de Dear et al., 1994). Data was collected for the ACT2

project between 1991 and 1995 at four sites. The Sunset Building (baseline and post-

retrofit) in San Ramon, Verifone (baseline) in Auburn and CSAA (post construction) in

Antioch. Antioch has a Mediterranean climate, less than 50 km inland from the San

Francisco Bay but separated from the water by the Berkeley Hills (nearest major city is

Concord). The season of this study was winter.

Sample buildings


Sample Size (n)


Floor Area

Occupancy Pattern

1 111 HVAC office building

Instruments

A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The

sensors chosen were selected to meet the response time and accuracy requirements of



ASHRAE Standard 55-81 and ISO Standard 7730 for thermal assessment. YSI series 700

probes with vinyl-coated tips were used to measure air temperature. Globe temperature was

measured by attaching a 38 mm diameter table tennis ball on the temperature sensors. The

balls were painted grey for correct emissivity. Air velocity was measured by Dantec 54R10

anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint

temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint

transducer. All parameters were measured at all three heights except dewpoint temperature

which was only measured at 0.6m. Radiant asymmetry and illuminance were also

measured, but are not essential to the purpose of RP-884.

Questionnaire

The questionnaire consisted of an on-line questionnaire, which addressed conditions at the

time physical measurements were being taken and a background questionnaire. The latter

covered subject details such as, health and emotional characteristics, office description,

work area and job satisfaction, environmental sensitivity, plus personal comfort, satisfaction

and perceived control. In the on-line section thermal sensation was rated on the 7-pt

ASHRAE scale. Thermal preference was assessed on a descriptive 3-pt scale. Thermal

acceptability was not rated. Metabolic rate was estimated based on a checklist referring to

the subjects activity in the 15 minutes before completing the on-line questionnaire, using

tables in the ASHRAE Handbook of Fundamentals (HOF, 1985). Clo estimates were based

on responses to the clothing item checklist provided in the on-line questionnaire from the

ASHRAE Standard 55-81 method.


Meteorological air temperature data at 600 hrs and 1500 hrs were purchased by RP-884

from the National Oceanic and Atmospheric Administration’s National Climatic Data Center.

Relative humidity, also at 600 hrs and 1500 hrs was extracted from the International Station

Meteorological and Climate Summary CD-ROM (ISMCS, 1992) for the nearest site.


The detailed methods and protocol used in ASHRAE RP-462 (and extended to the

ASHRAE RP-702 project described above) were carried out in full for the ACT2 Project.

Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462, little



standardisation was necessary. However, clothing was based on the ASHRAE 55-81

method, and so required conversion into equivalent ASHRAE 55-92 values. 0.15 clo was

then added for chair insulation. The research design of this field experiment was longitudinal,

so for the purposes of RP-884, independence between subjects was assumed.



C.5. Project Title - Higher PMV causes higher energy consumption in air-

conditioned buildings: A case study in Jakarta, Indonesia.


Tri H. Karyono (University of Sheffield, UK). This is a CLASS-3 field experiment.


This project is disseminated as file numbers 6 (summer - HVAC bdgs), 7 (summer - NV)

and 8 (summer - mixed mode buildings) in the RP-884 database.


Karyono, T. H. (1995) “Higher PMV causes higher energy consumption in air-conditioned

buildings: A case study in Jakarta, Indonesia, “ Standards for thermal comfort. ed by Fergus

Nicol, Michael Humphreys, Oliver Sykes and Susan Roaf. Chapman and Hall pp 219-226.

Karyono, T. (1996) “Thermal comfort in the tropical southeast Asia region.” Architectural

Science Review. V39(3), pp.135-139.

Karyono, T.H (1996) “Discrepancy between actual and predicted thermal votes of

Indonesian workers in Jakarta, Indonesia.” International Journal of Ambient Energy.

V.17(2), pp.95-100.


The project was located in Jakarta, Indonesia. This is in a wet equatorial climate zone with a

season classified as “summer” all year round.

Sample buildings


Sample Size (n)


Floor Area

Occupancy Pattern

1 97 NV office building 2 103 HVAC office building 3 98 HVAC office building 4 96 HVAC office building 5 91 HVAC office building 6 41 Mixed (hybrid) office building 7 70 HVAC office building



Instruments

Bruel and Kjaer 1212 Thermal Comfort Meter. No anemometer used in this project.

Relative humidity were measured with a solid state hygrometer.

Questionnaire

In Bahasa Indonesian.


Outdoor climatological air temperature and relative humidity data at 600 hrs and 1500 hrs

were obtained from the International Station Meteorological and Climate Summary (ISMCS,

1992) CD-ROM for Jakarta.


The B+K 1212 instrument was used to measure operative and equivalent temperatures. As

a result no radiant temperatures could be calculated (from globe temperature). Clothing

estimates were based on the Bruel and Kjaer manual which closely corresponds to the ISO

7730 (1984) methods and which was mapped to the ASHRAE 55-92 standard for RP-884.

Chair insulation estimates of 0.15 clo were also added to form a total insulation variable in

the RP-884 database. The research design was cross-sectional which satisfied the

assumptions for RP-884, that all subjects were independent.



C.6. Project Title - Montreal ASHRAE RP-821.

“Field Study of Occupant Comfort and Office Thermal Environments in a Cold Climate.”

This is the third of a series of ASHRAE projects (following RP-462 in San Francisco and

RP-702 in a hot-humid climate).


Giovanna Donnini, Jean Molina, Carlo Martello, Dorothy Ho Ching Lai, Kit Ho Lai, Ching Yu

Chang, Michel Laflamme, Van Hiep Nguyen, Fariborz Haghighat (Auger, Donnini and

Nguyen Inc.). This is a CLASS 1 field experiment in line with the preceding two ASHRAE-

sponsored field experiments.





Donnini, G. et al (1996) Field Study of Occupant Comfort and Office Thermal Environments

in a Cold Climate: Final Report. ADN Inc., Montreal, Quebec, Canada.


The cities chosen for the study are Montreal, Longueuil, Gramby, Cap-de-la-Madeleine,

Shawinigan, Trois-Rivieres, Hull and Maniwaki in Canada. They are all located along the

border of the Northern and Southeastern limits. The climatic classification is towards the

cold extreme of the humid mid latitudes. Data were collected in both summer and winter

seasons.

Instruments

Air temperatures were measured using Dantec 54R10 thermistors. Globe temperatures

were measured using Bruel and Kjaer globe temperature sensors (MM 0030) each

consisting of a Pt100 (platinum resistance) temperature sensing element situated in the

centre of a 150mm diameter globe of appropriate emissivity. Air velocity and turbulence

were measured by Dantec 54R10 anemometers, which are omnidirectional fully

temperature-compensated sensors. The factory calibrated the sensors the week preceding



the start of the site visits. Dew point temperature and vapour pressure was measured by a

Bruel and Kjaer air humidity transducer (MM 0037). Air temperature, globe temperature, air

velocity and turbulence were measured at three heights (ankle, waist and head height) and

dew point temperature was measured only at waist height.

Sample Buildings

Building Code and Location

Sample Size (n)

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

1 Montreal

summer 39 winter (37)

free cooling, VAV.

7220m2. 5 storey and sub-basement. Mainly open plan

Department of Provincial Government (offices).

2 Montreal

39 (38)

double duct, CAV.

68198m2. 15 storeys and sub basements. Mainly open plan.

Department of Provincial Police (jail).

3 Cap-de-la-Madeleine

40 (39)

free cooling, CAV.

3963m2. 3 storeys. Mainly open plan.

Department of Provincial Police (police station).

4 Shawinigan

41 (40)

free cooling, VAV and CAV.


Department of Provincial Government (court).

5 Trois-Rivieres

44 (44)

free cooling, VAV.

10,451m2. 5 storeys. Mainly open plan.


6 Longueuil

41 (40)

free cooling, VAV.



7 Longueuil

40 (40)

double duct, VAV and free cooling, CAV.

12,500m2. 8 storeys and sub-basement. Mainly open plan.


8 Maniwaki

31 (30)

free cooling, VAV

3500m2. 2 storeys and sub-basement. Mainly open plan.


9 Gramby

40 (39)

double duct, VAV.

8784m2. 3 storeys and sub-basement. Mainly open plan.


10 Montreal

40 (39)

free cooling, VAV.



11 Hull

42 (40)

double duct, VAV and CAV.



12 Montreal

6 (none)

double duct, CAV and free cooling, VAV.


Private, professional and advertising offices.

Questionnaire

The questionnaire used here was essentially the same as the one used in Townsville

(ASHRAE RP-702 Hot Humid Field Experiment). The subjective survey was divided into

two parts, Background and Online. The Background questions covered areas such as



demographics, contextual and psychological factors. The on-line questions were related to

the subjects assessment of their immediate thermal environment at that point in time and

was answered at the time the physical measurements were being taken. Sensation ratings

were based on the ASHRAE 7-pt scale. Thermal acceptability was addressed as a yes/no

response and thermal preference was assessed on a 3-pt scale. Metabolic rating was

based on the ASHRAE 55-92 Standard and ISO 7730 Standard. Met was assessed over

four distinct time periods from which an overall metabolic value was obtained. Clo was

estimated using the ASHRAE Standard 55-92 checklist. Adaptive behaviour questions were

also addressed regarding the subjects perceived control over their thermal environment.


The meteorological data recorded in the original field experiment data included; hourly

temperatures, wind speed and direction, relative humidity, daily precipitation, start and stop

times of precipitation and general conditions. These recordings were purchased by the

researchers from the closest met observation site to each building tested. For the purpose

of RP-884 air temperatures and relative humidities at 600 hrs and 1500 hrs were extracted

for use.


Due to the use of a 150 mm diameter globe with slow response time for measuring globe

temperature, there was uncertainty as to whether or not the instrument achieved thermal

equilibrium within the exposure time. Therefore, all rows where |TAAV-TRAV| >= 2 K were

deleted from the data set before analysis continued. Clo was estimated by the ASHRAE

55-92 Standard so no correction was necessary and allowances for the insulation due to a

chair had been made. The research design was cross-sectional.



C.7. Project Title - Richard de Dear’s PhD research project in Australia.


Dr Richard de Dear and Andris Auliciems (University of Queensland). This is a CLASS-2

investigation .


This project is disseminated as file numbers 11 (Brisbane, summer - HVAC), 12 (Brisbane,

summer - NV), 13 (Darwin, summer “dry” - HVAC), 14 (Darwin, summer “wet” - HVAC), 15

(Melbourne, summer - HVAC) and 16 (Melbourne, summer - NV) in the RP-884 database.


de Dear, R. J. and A. Auliciems (1985) “Validation of the Predicted Mean Vote model of

thermal comfort in six Australian field studies.” ASHRAE Trans., V. 91(2), pp. 452-468.

de Dear, R. J. and A. Auliciems (1985). Thermal neutrality and acceptability in six Australian

field studies, Clima 2000, Indoor Climate (P.O. Fanger, editor), Vol. 4:103-108. VVS

Kongress-VVS Messe, Copenhagen.

de Dear, R. J. (1985) Perceptual and adaptational bases for the management of indoor

climate. (St Lucia Queensland: University of Queensland PhD thesis).

de Dear, R.J. and A. Auliciems (1986). Air conditioning in Australia II: User attitudes. Arch.

Science Review, vol. 31, pp. 19-27.


This project was conducted in three major cities, located in three distinct climate zones

across Australia. Samples from both HVAC and NV buildings were taken in Brisbane

(humid subtropical climate) and Melbourne (west coast marine climate) during summer.

Samples were also taken from HVAC buildings in Darwin (tropical savanna or wet/dry

tropics) during the “dry” and “wet” seasons.

Instruments

Wet and dry bulb temperatures were recorded with an Assmann aspirated psychrometer.

Globe temperatures were recorded using a Zeal mercury-in-glass thermometer



(manufactured according to British Standard 2842/66) inserted in the centre of a 40mm ping

pong ball painted matt black. Air speeds were measured at three heights within the

occupied zone but only an average was recorded. The anemometers were Kurz 441M with

manufacturer’s claimed accuracy being ±0.03 m s-1.

Sample Buildings


(blcode)

Sample Size (n)

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

Brisbane 1 195 HVAC office building Brisbane 2 102 HVAC office building Brisbane 3 69 HVAC office building Brisbane 4 114 HVAC office building Brisbane 5 84 HVAC office building Brisbane 1 157 NV office building Brisbane 2 124 NV office building Brisbane 3 69 NV office building Brisbane 4 211 NV office building Brisbane 5 49 NV office building Darwin-dry 1 14 HVAC office building Darwin-dry 2 12 HVAC office building Darwin-dry 3 131 HVAC office building Darwin-dry 4 82 HVAC office building Darwin-dry 5 97 HVAC office building Darwin-dry 6 52 HVAC office building Darwin-dry 7 53 HVAC office building Darwin-dry 8 50 HVAC office building Darwin-wet 8 48 HVAC office building Darwin-wet 9 85 HVAC office building Darwin-wet 10 100 HVAC office building Darwin-wet 11 58 HVAC office building Darwin-wet 12 157 HVAC office building Darwin-wet 13 58 HVAC office building Darwin-wet 14 49 HVAC office building Melbourne 1 83 HVAC office building Melbourne 2 243 HVAC office building Melbourne 3 102 HVAC office building Melbourne 4 84 HVAC office building Melbourne 1 126 NV office building Melbourne 2 411 NV office building Melbourne 3 16 NV office building

Questionnaire

Thermal sensation was assessed on the ASHRAE 7-point linear scale. Thermal preference

was registered on a symmetrical 7-point scale (-3, -2, -1, 0, +1, +2, +3). Metabolic

checklists were applied to the last 10 minutes, between 20 and 10 minutes ago, between 30



and 20 minutes ago and between 60 and 30 minutes ago. The average metabolic estimate

across the last hour was recorded in the data file.


Actual meteorological data (temperature and humidity) corresponding to the date stamped

on each questionnaire were purchased from the Australian Bureau of Meteorology.


The 7-point preference scale was converted to the McIntyre scale so that votes of -3, -2 and -

1 were “want cooler,” a vote of 0 was counted as “no change,” and votes of +1, +2 and +3

counted as “want warmer.” Clothing insulation was converted from the McIntyre 1980

method to the equivalent ASHRAE (1992) value and 0.15 clo was added for chair insulation

to all cases with sedentary metabolic rates. The research design was cross-sectional which

satisfied the assumptions for RP-884, that all subjects were independent.



C.8. Project Title - A field study of thermal comfort using questionnaire software.


Guy R. Newsham, PhD. and Dale K. Tiller D.Phil. (National Research Council Canada). This

is a CLASS-3 field experiment.


This project is disseminated as file number 17 (winter - HVAC) in the RP-884 database.


Newsham, G. R. and D. K. Tiller. (1995) A field study of Thermal Comfort using

questionnaire software. IRC Internal Report. No 708.

Newsham, G. R., D. K. Tiller. (1996) Questionnaire Software to Enable Study of Short-term

Changes in Subjective Reactions to the indoor Environment. IRC Internal Report.


Ottawa, Canada. The location is borderline between humid mid latitude and continental

subarctic. The investigation was performed in winter.

Sample buildings

Building Code

(blcode)

Sample Size (n)

Climate Controls

(bldgtype)

Floor Area and

layout

Occupancy Pattern

1 390 HVAC 3 storey, open plan office on part of 2nd floor.

Federal government. Facilities design work.

2 437 HVAC 7 storey, open plan office on part of 1st floor.

Federal government. Variety of bibliographic tasks.

3 988 HVAC 20 storey, mostly open plan on 7th and part of 9th floor.

Federal government. Variety of administrative, technical and scientific tasks.

4 44 HVAC 3 storey, open plan office on part of 2nd floor.

Federal government. Variety of bibliographic tasks.

Instruments



Indoor climatic instrumentation consisted of an ACR “SmartReader” thermistor for

temperature and a solid-state hygrometer to measure humidity. Measurements were made

at waist height only and the variables air speed and globe temperatures were not measured.

Questionnaire

The questionnaire was software based addressing 5 questions: environmental conditions at

the time of physical data collection, sensation/comfort rating on a 7-pt scale, thermal

preference, questions regarding adaptive behaviour and clo estimations. Total clothing

ensemble worn by the subjects was estimated using the ASHRAE 55-92 checklist. Thermal

acceptability and activity or any form of metabolic rating was not provided.


Outdoor meteorological data including air temperature and humidity (RH%) was measured

by the campus weather station. Three of the study sites were on the same campus as the

station, the fourth was located 10km away. Meteorological data provided with the original

dataset was that closest to the time when the questionnaire was being answered. From this

information our dayta_15 and dayrh_15 variables were extracted. Also provided in the

original data set was outdoor air temperature and humidity at 8:00am from which our

dayta_06 and dayrh_06 variables were obtained.


The research design for this study was longitudinal, but it was assumed for the purpose of

RP-884 that all subjects were independent (i.e. assumed cross-sectional). Clo was

estimated using the ASHRAE 55-92 Standard so no corrections were necessary. However

clo was measured at the beginning of the day and so to more closely approximate the total

clothing ensemble at the time of the questionnaire, the clo change variable in the original

data set was used for adjustments. This variable specified at the time of the questionnaire

wether the subject had had a major or minor clothing change (+ - 0.34 clo and + - 0.05 clo)

since the morning. These adjustments were made and then 0.15 clo added for the insulation

provided by a chair to give a total insulation as a separate variable. Age was given as the

end point of a bin, but was replaced with the midpoint value. While metabolic rates were not

recorded, a default value of 1.2 mets was temporarily inserted into the file for the purposes

of index calculation, but then removed from the database.



C.9. Project Title - “Thermal comfort in Pakistan.”

This project was part of the 1993 Oxford Brookes University field project for The National

Energy Agency Conservation Centre (ENERCON) agency of the Pakistan Government

investigating the reduction of energy consumption in buildings and an adaptive model of

thermal comfort.


Nicol, J. F., G. N. Jami, O. Sykes, S. Roaf, M. Humpherys and M. Hancook (School of

Architecture, Oxford Brooks University). This is a CLASS-3 field experiment.


This project is disseminated as file numbers 18 (Karachi, summer - NV), 19 (Karachi, winter

- NV), 20 (Multan, summer - NV), 21 (Peshawar, summer - NV), 22 (Peshawar, winter - NV),

23 (Quetta, summer - NV), 24 (Quetta, winter - NV), 25 (Saidu, summer - NV) and 26 (Saidu,

winter - NV) in the RP-884 database.

Project Publications

Nicol, J. F., G. N. Jami, O. Sykes, M. Humpherys, S. Roaf and M. Hancock. (1994) Thermal

Comfort in Pakistan. Oxford Brookes University.


This study was conducted across five cities in Pakistan including Karachi (Lower Indus

Plain), Quetta (Baluchistan Plateau), Multan (southern Upper Indus Plain), Peshawar

(northern Upper Indus Plain) and Saidu Sharif (northern mountains).

Karachi is the capital of the Sindh province and the largest city in Pakistan in terms of

population and size. Karachi is also a major Arabian Sea Port. Being only 4m above sea

level warm moist air blows in from the Indian Ocean, however this does not often result in

precipitation. Karachi is quite humid compared to the rest of the country and this is borne

out by relatively small diurnal and annual temperature ranges (the monthly mean temperature

varies just 11°C in Karachi and generally by 21°C to 25°C in other parts). Karachi has an

average temperature maxima and minima of 33°C and 27°C respectively in July and 25°C



and 13°C respectively in January. Karachi falls under a desert climate classification

despite it’s location in a coastal zone.

Multan is a major city on the southern Upper Indus Plain in the Punjab, surrounded by the

desert region of Pakistan. However recent irrigation projects have resulted in microclimatic

changes which have resulted in increases in rainfall with some associated changes in

temperature and humidity. Historical records of temperature maxima and minima are 21°C

and 6°C respectively in January and 42°C and 29°C respectively in June or 40°C and 29°C

respectively in July. The climate zone for Multan is “desert.”

Peshawar is the capital of the North/West Frontier Province and is at the northern end of the

Upper indus plain at an elevation of 359m. The temperatures in Peshawar are fairly similar

to those of Multan. The average maxima and minima are 17°C and 4°C respectively in

January and in June 41°C and 25°C respectively or 40°C and 26°C respectively in July.

The climate zone for Peshawar is semi desert.

Quetta is the capital city of the Baluchistan province and is situated on the north-western

Afghanistan boarder of Pakistan. The city is located at an altitude of 1692m on a dry desert

plateau surrounded by mountains rising over 2500m high. Due to its elevation it is cooler

than Peshawar and Islamabad, but has considerable temperature fluctuations on a daily and

seasonal scale. The rainfall in Quetta is very low as is its humidity because of the

surrounding desert. Average temperature maxima and minima are 10°C and -2°C

respectively in January and 35°C and 18°C respectively in July. Quetta is classified as

being in a cool semi desert climate zone.

Saidu Sharif is a town in the northern hills at an elevation of about 1000m. Surveys were

carried out in Mingora a “twin town” about a mile from Saidu Sharif. Specific climatological

data for the two towns was not able to be obtained. The main factor however for both towns

are their elevations giving mean temperature maxima and minima of 14.3°C and 2.2°C

respectively in January and 36.4°C and 20.8°C respectively in June. The climate zone for

Saidu can be described as semi desert.

Season - The project was divided into two surveys, one in summer (July 1993) and the other

in winter (December 1993 - January 1994) each extending over about a week.



Sample buildings

This table indicates only one building per city in Pakistan. In actual fact there were many

buildings, including residences and offices. In the vast majority of cases, there was only one

subject per building. In many cases the subjects were monitored during occupancy of more

than a single building, making the data incompatible with the RP-884 structure. Therefore,

for simplicity, all buildings within a particular city are treated as a single building.


(blcode)

Sample Size (n)

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

Karachi 1 - summer 1 - winter

190 470

NV residential houses and office buildings

Multan 2 - summer 437 NV residential houses and office buildings

Peshawar 3 - summer 3 - winter

556 513


Quetta 4 - summer 4 - winter

492 425


Saidu Sharif 5 - summer 5 - winter

568 548


Instruments

Indoor climatic instrumentation was recorded by a portable datalogger. Relative humidity

and air temperature were monitored by a Hanna Instruments probe. This consisted of a

polished aluminium sheath 19mm in diameter, containing in its ventilated tip a humidity

sensor (solid-state hygrometer) and a thermistor. The instrumentation measured air

temperature, globe temperature and humidity. The globe thermometer had a 38mm

diameter ping pong ball with appropriate emissivity attached over the sensor. All variables

were measured at subjects’ waist height.

Questionnaire

The questionnaire addressed conditions at time of physical measurements. Time lapse

between instrument measurements and questionnaire response was never more than 10

minutes. Comfort was rated using the 7-pt semantic differential based on Bedford. Thermal

preference was rated on a want to be warmer/cooler descriptive scale and thermal

acceptability questions were not considered. Other thermal environmental parameters

included were air movement, draft and skin moisture. Metabolic activity was based on a

descriptive scale and noted at the time the questionnaire was being carried out. Total



clothing ensemble insulation experienced by the subject was estimated using the ISO 7730

checklist and work of McCullough (eg 1985) and others.


Daily outdoor maximum and minimum temperatures were obtained for a number of the

centres from the Pakistan Meteorological office for July and December 1993 and January

1994. Where temperatures were not provided they were replaced with climatological data

(monthly means) from the International Station Meteorological and Climate Summary Vol.2

CDROM (ISMCS, 1992). All outdoor humidities were also obtained from this source and

had to be derived from mean dewpoint temperature and mean temperature minima and

maxima.


The Bedford 7-point thermal comfort scale was mapped directly to the ASHRAE 7-point

thermal sensation scale for RP-884 purposes. The data was presented as subjects in

individual houses, with studies conducted in summer and winter, so the project was of

longitudinal research design. For the purpose of this study all houses in the same city were

considered to be identical buildings, thus it was assumed there was a number of subjects

from one building for each city and the subjects were independent between both the summer

and winter studies. Some indices in the original data set had to be re-defined to conform to

RP-884 standards. Clo was estimated by ISO 7730 (1984) and corrected to the ASHRAE

55-92 Standard using the regression models developed within RP-884. The activity variable

in the original data set was used such that if activity was <= 4 then 0.15 clo was added to the

total clothing ensemble to form another variable (insul), that accounted for the additional

insulation provided by a chair for subjects that were seated. Velocity measurements in the

raw data file indicated a systematic bias that was time-dependent. The original data in all

summer files was found to be less affected and so original data were used. In the winter

files, values >1.5 m/s were replaced with an average. The Multan, Winter field experiment

was omitted from the RP-884 database.



C.10. Project Title - Comfort criteria for passively cooled buildings. a PASCOOL

task.


N. Baker and M. Standeven, The Martin Centre for Architecture and Urban Studies,

University of Cambridge, UK. This is a CLASS-2 field experiment.


This project is disseminated as file number 27 (summer - NV) in the RP-884 database.


Baker, N and M. Standeven. (1995) “A Behavioural Approach to Thermal Comfort

Assessment in Naturally Ventilated Buildings”. Proceedings from CIBSE National

Conference, Ch 76-84.

Baker, N. and M. Standeven. (1994) Comfort criteria for passively cooled buildings. A

PASCOOL task. Renewable Energy. V 5. n 5-8 Aug 1994. p 977-984.


This field experiment was carried out in Athens, Greece for the summer season.

Athens has a Mediterranean climate.

Sample buildings


Sample Size (n)


Floor Area

Occupancy Pattern

1 409 NV residential building 2 276 NV residential building 3 443 NV residential building 4 176 NV residential building 5 187 NV residential building 6 135 NV residential building

Instruments

Indoor room climate instrumentation included: a thermistor to measure air temperature, an

omnidirectional hot-wire sensor to measure air speed, a solid-state hygrometer to measure

humidity and a globe thermometer with 38mm diameter ping pong ball to measure globe

temperature.



Local climate instrumentation consisted of: a calibrated sensor array comprising air

temperature thermistor, omnidirectional thermistor anemometer and two hemispherical

globe thermometers, mounted on a headset similar to that of a walkman. Data was logged

on a portable logger allowing complete thermal histories to be recorded for the day,

including time when the subject was away from the room.

The local data (headsets) were attached ot questionnaire responses in the RP-884

database file for this PASCOOL project. In cases where local data were unsuitable or

unavailable, room data were substituted.

Questionnaire

The questionnaire addressed the conditions at the time physical measurements were being

taken. Sensation was rated on the ASHRAE 7-pt scale. Questions of thermal acceptability

and thermal preference where both considered and metabolic ratings were taken. Clothing

insulation was estimated using the ISO 7730 checklist. Adaptive behaviour questions

regarding changes in clothing and adjustment to controls such as opening or closing shades,

blinds or windows and relocations within the room were recorded.


Outdoor Meteorological air temperature data was recorded simultaneously with indoor

measurement made. For the purposes of RP-884 outdoor temperatures at 600 hrs and

1500 hrs were extracted. Humidities at 600 hrs and 1500 hrs were obtained from the

International Station Meteorological and Climate Summary (ISMCS, 1992) CDROM.


This project was of longitudinal research design, but for the purposes of RP-884 subjects

were assumed to be independent (ie. cross-sectional). Clothing insulation was estimated

using the ISO 7730 (1984) Standard, it was therefore necessary to adjust clo to conform to

the ASHRAE 55-92 Standard. Also where the metabolic rate was <= 2 met it was assumed

the subject was seated and so 0.15 clo was added to the total clothing ensemble in these

cases to account for the insulation provided by a chair. The 5-pt variable PRF_VOTE in the

original data was re-coded to our 3-pt McIntrye (MCI) scale. Where air velocity was missing

0.1 m/s was temporarily inserted for the software based index calculation and then removed

from the database.



C.11. Project Title - Developing indoor temperatures for naturally ventilated

buildings.


I. A. Raja, J. F. Nicol and M. A. Humphreys (Oxford-Brookes University, UK). This is a

CLASS-3 investigation.


Nicol, J. F., M. A. Humphreys and I. A. Raja (1995). “Developing Indoor Temperatures for

Naturally Ventilated Buildings”. Proceeding for CIBSE National Conference.

Also see the Full Report.


This project is disseminated as file number 28 (summer - NV) in the RP-884 database.


The project is located in Oxford, South Britain about 63m above sea level and is situated at

51o 46’ North and 1o 16’ West. The climate of Oxford is typical of the low lying part of the

English midlands and is also influenced by its proximity to the Atlantic. Oxford experiences

one of the warmer maxima in the surrounding area with a mean maximum temperature of

21.7°C in July. The mean minima of 1.3°C in January and February reflects weather similar

to that of the midlands and south-east. This field experiment was completed in the summer

months of August and September and comes under the climate classification of west coast

marine.

Sample buildings


Sample Size (n)

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

1 496 NV School of Architecture plus Biological and Molecular Sciences

2 334 NV Headington Hill Hall.

3 47 NV Tonge building.



Instruments

Air temperature was measured using a thermistor. An adapted thermistor probe with a

38mm diameter ping pong ball of suitable emissivity attached, was used to measure globe

temperature. Air speed was registered using an omnidirectional sensor and a solid-state

hygrometer was used to measure humidity. All measurements were taken at waist

(generally desk) height.

Questionnaire

A comfort rating on the 7pt Bedford scale was addressed in the questionnaire as well as

thermal preference. Thermal acceptability was not recorded. Metabolic ratings were taken

at the time the questionnaire was being answered, but covered the 15 minute period before

the questionnaire was completed. Clothing insulation estimates were based on the ISO

7730 checklist with the insulation effects of chair included in the total clothing ensemble of

the subject. Questions of adaptive behaviour and perceived control on a subjects thermal

environment were addressed. Specifically, whether doors, window and curtains or blinds

could be opened and closed as well as the influence of fans and heater that could be

switched on or off.


Outdoor Meteorological data was obtained for every 0.25 hours from the Oxford University

Radcliffe Observatory by the original researchers. From this, air temperatures and relative

humidities at 600 hours and 1500 hours were extracted for the purposes of RP-884.


The research design of this project was longitudinal, but for RP-884 purposes all subjects

were assumed to be independent (ie. cross-sectional). The Bedford 7-point thermal comfort

scale was mapped directly to the ASHRAE 7-point thermal sensation scale for RP-884

purposes. Clothing insulation, estimated using the ISO 7730 (1984) Standard was

corrected to the ASHRAE 55-92 Standard via regression models developed within RP-884.

Allowance for the insulation provided by a chair was incorporated into the total clothing

ensemble by the original researchers only when the subjects reported themselves as seated.

This provided the RP-884 insul variable. To obtain clothing insulation (clo) in isolation, 0.15

clo was subtracted. All rows with missing air temperature were deleted, but where velocity



was missing, 0.1 m/s was temporarily substituted and where indoor relative humidity and

metabolic rate were missing, 50% and 1 met respectively were temporarily substituted for

the purposes of index calculations and then removed from the database.



C.12. Project Title - Mixed mode climate control: some hands-on experience.


David Rowe. Department of Architectural and Design Science, Sydney University, Australia.

This is a CLASS-2 investigation


This project is disseminated as file numbers 29 (Summer - Mixed Mode), 30 (winter - Mixed

Mode) and 31 (winter - HVAC) in the RP-884 database.


Nothing published yet.


The field experiment was conducted in Sydney, the capital of the state of New South Wales

in Australia. Sydney’s climate is humid and sub-tropical. The project conducted in both

summer and winter seasons.

Sample buildings


Sample Size (n)

and season


Floor Area

Occupancy Pattern

1 137 - summer 170 - winter

Mixed (hybrid) university offices

2 83 - winter HVAC administration offices

Instruments

RTD devices were used to measure air temperature. No globe temperatures were

measured but mean radiant temperature was provided based on the average of six

orthogonal plane radiant temperatures, areally weighted for the projection area factors of the

human body. Air speed was assessed using an omnidirectional sensor and included

turbulence intensity measurements (> 10Hz). A chilled-mirror dewpoint sensor was used to

measure humidity. All measurements were taken at a single height.

Questionnaire



The questionnaire for this project was based directly on that used for the ASHRAE RP-702

Hot Humid Field Experiment in Townsville Australia (see above for de Dear et al., 1994).

Thermal sensation rated on the 7-pt ASHRAE scale was recorded at the time physical

measurements were being taken, along with the other items on the questionnaire that follow.

Thermal acceptability and thermal preference was addressed. Metabolic ratings at the time

of and one hour before the questionnaire were recorded. The total clothing ensemble

insulation was estimated using the ASHRAE 55-92 checklist. Other thermal environmental

parameters considered include air movement.


Outdoor Meteorological Data consisting of air temperature and relative humidity at 600

hours and 1500 hours was obtained for this field experiment from Macquarie University’s

Meteorological site, Sydney, Australia.


The research design for this project was longitudinal and for the purpose of RP-884 all

subjects were assumed to be independent (ie. cross-sectional). Clothing insulation was

estimated from ASHRAE 55-92 checklists so no alterations were necessary apart from the

addition of 0.15 clo to account for the insulation effects of a chair in creating our insul

variable. Throughout the field experiment where mean radiant temperature was not provided

air temperature was entered as a substitute.



C.13. Project Title - ASHRAE sponsored RP-462. San Francisco area.


Gail Schiller, Edward Arens, Fred Bauman, Charles Benton, Marc Fountain and Tammy

Doherty (CEDR at University of California, Berkeley). This is a CLASS-1 field experiment


This project is disseminated as file numbers 32 (summer - HVAC), 33 (summer - NV), 34

(winter - HVAC) and 35 (winter - NV) in the RP-884 database.


Schiller, G. E., E. Arens, F. Bauman, C. Benton, M Fountain and T. Doherty. (1988) A Field

Study of Thermal Environments and Comfort in Office Buildings: Final Report--ASHRAE

462. (CEDR:UC Berkeley).

Schiller, G. E. (1990) A comparison of measured and predicted comfort in office buildings.

ASHRAE Transactions, 96(1).


RP-462 was conducted over five locations within the San Francisco Bay area including

Berkeley, San Ramon, Palo Alto, San Francisco and walnut Creek. All five cities are within a

Mediterranean climate zone, but all have different local climates due to their location around

the San Francisco Bay area. San Francisco is located right on the coast, but also very close

to the Bay. Palo Alto is situated further from the coast close to southern end of the Bay and

behind the Santa Cruz Mountains. Berkeley is located across the Bay from the Golden Gate

and Walnut Creek is further inland almost directly east of Berkeley. San Ramon is a similar

but shorter distance from the Bay as Walnut Creek, but instead it is almost directly east of

San Francisco. The field experiments were conducted across both summer and winter

seasons.



Sample buildings

Location Buildg Code

(blcode)

Sample Size

(n) and Season*

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

Berkeley 1 122 - S 121 - W

NV 236,600 ft2. crowed open plan offices.

San Ramon 2 119 - S 123 - W

HVAC - thermal ice storage and evap. ponds.

2,000,000 ft2. office building

Palo Alto 3 92 - S 101 - W

HVAC (multizone HVAC with EMS)

187,000 ft2. mostly private offices.

San Francisco

4 108 - S 134 - W

HVAC - heat pump mech. system.

191,000 ft2. open plan with private balconies on perimeter.

San Francisco

5 115 - S 132 - W

roof-mounted HV unit, no mech. a.c.,

54,000 ft2. open plan converted factory.

San Francisco

6 123 - S 136 - W

NV 90,000 ft2. open plan and private offices.

San Francisco

7 107 - S 122 - W

HVAC - thermal ice storage, VAV perimeter reheat.

265,000 ft2. open plan and private offices.

San Francisco

8 117 - S 147 - W

HVAC 634,000 ft2. large open plan.

Walnut Creek

9 23 - S 145 - W

HVAC 316,400 ft2. open plan and private offices.

Walnut Creek

10 107 - S 146 - W

HVAC 368,000 ft2. open plan with partitions and private offices.

* S = summer, W = winter in the sample size and season column.

Instruments

Air temperature, air velocity, humidity, and globe temperatures were measured using a

mobile cart at the heights indicated below, with the exception of the one stationary

observation point. Air temperature was measured with a shielded platinum RTD at 0.6m

and shielded type T thermocouples at 0.1m, 0.6m and 1.1m were used. Air velocity was

measured by an elliptical omnidirectional constant temperature anemometer at 0.6m and

spherical omnidirectional temperature compensated anemometer at 0.1m and 1.1m.

Humidity was measured by a chilled-mirror dew point sensor at 0.6m. Globe temperatures

were measured by a type T thermocouple inside a 38 mm diameter table tennis ball (painted



grey) at heights of 0.1m, 0.6m and 1.1m on the mobile cart and at 1.1m in the stationary set

up. Other variables measured not of relevance to RP-884 include radiant temperature

asymmetry, surface temperature and illumination.

Questionnaire

Questionnaire responses were collected at the time physical measurements were being

taken. The ASHRAE 7-pt scale was used to determine thermal sensation. The McIntyre

scale was used to assess thermal preference. Thermal acceptability was not addressed.

Metabolic rating and clothing insulation estimates were based on checklists in ASHRAE

Standard 55-81 (1981). The background section of the survey (not necessarily completed

when physical measurements were being made) covered general descriptions of office work

areas; degree of satisfaction with components of their work environment; personal and

comparative comfort and personal subject related information.


Outdoor Meteorological air temperature minima and maxima were purchased from the US

National Climate Data Center (NCDC) for sites considered of similar climatic situations to

the study locations. Where a suitable site could not be requisitioned, climatological data was

extracted from the International Station Meteorological and Climate Summary (ISMCS,

1992) CDROM. All climatological humidity data were also obtained from ISMCS (1992).


RP-884 is the fourth ASHRAE sponsored project in the series RP-462, RP-702 and RP-

821. A lot of the assumptions and standards of RP-462 project have formed the basis for

the later projects including RP-884, thus limited standardisation has been necessary here.

Clothing insulation was converted from ASHRAE 55-81 to the 55-92 standard. 0.15 clo was

added to the total clothing ensemble for the insulation effects of a chair to create our insul

variable. The research design of this project was part longitudinal and part cross-sectional,

but for RP-884 purposes all subjects were assumed to be independent.



C.14. Project Title - A field investigation of thermal comfort environmental

satisfaction and perceived control levels in UK office buildings, University of

Liverpool.


This project is disseminated as file numbers 38 (summer -NV), 39 (winter - NV) and 40

(winter - Mixed Mode) in the RP-884 database.


Ruth N. Williams (The Building Services Research and Information Association, Berkshire,

UK). This is a CLASS-2 investigation


Williams, R. N. (1995). A field investigation of thermal comfort environmental satisfaction

and perceived control levels in UK office buildings. Healthy Buildings. Vol. 3 pp. 1181-1186.

Williams, R (1996) “Predicting environmental dissatisfaction in UK offices,

“CIBSE/ASHRAE Joint National Conference, Harrogate UK, VII., pp.167-178.


This project was conducted across three towns/cities in the UK, including Liverpool, St

Helens and Chester. All three come under the west coast marine climate classification. The

study was carried out in summer and winter months.

Instruments

Air temperature was measured using thermistors and an omnidirectional hot bead sensor to

measure air speed. A Envirlog supplied sensor (type unknown) was used to measure

humidity and by attaching 38mm diameter ping pong balls globe temperature was also

measured. Air Speed and humidity were measured at waist height. Air temperature and

globe temperature were measured at all three heights (ankle, waist and head), but provided

to the RP-884 database as a single average.



Sample buildings


(blcode)

Sample Size (n)

and season

Climate Controls

(bldgtype)

Floor Area

Occupancy Pattern

Liverpool 1 19 - summer NV office buildings A&B

St Helens 2 8 - summer NV LC

St Helens 3 140 - summer 31 - winter

NV WH

St Helens 4 121 - winter Mixed (hybrid)

NWB

Chester 5 44 - winter NV CCH

Chester 6 31 - winter NV COM

Chester 7 67 - winter NV ANN

Liverpool 8 36 - winter NV SEN

Questionnaire

The questionnaire addressed both conditions at the time of physical measurements and

typical overall conditions. Thermal sensation was rated using a 7-pt ASHRAE scale.

Thermal comfort was rated using the 7-pt Bedford scale. Thermal acceptability was

addressed but not thermal preference. Metabolic rating was dealt with by asking if the

subject was sitting or standing during most of their work time, from which an estimate was

derived. Clothing insulation estimates were based on the ISO 7730 (1994) checklist with

corrections for the insulation from a chair included. Adaptive behaviour questions of the

subjects perceived control on temperature, humidity, freshness, smell, appearance, lighting,

noise and layout within their working environment was noted.


Outdoor Climatological air temperature data at 600 hours and 1500 hours was obtained

from Weather (the journal, for site - Ringway). Relative humidity at 600 hours and 1500 hours

was obtained from the International Station Meteorological and Climate Summary (site -

Liverpool) CDROM.


The research design of this study was cross-sectional which satisfies the assumption of

independence between subjects for RP-884. Coding conventions for some variables was



altered to conform to RP-884 definitions. Clothing insulation estimated using ISO 7730

(1984) checklists, was corrected to follow the ASHRAE 55-92 Standard. The sex (gender) of

subjects was not indicated in the study so an average of the adjusted clo to the ASHRAE 55-

92 Standard for males and female was used in all cases. 0.15 clo was then subtracted from

this corrected clothing plus chair insulation to create our clo variable.



C.15. Project Title - Thermal comfort in the humid tropics: field experiments in air

conditioned and naturally ventilated buildings in Singapore.


R. J. de Dear, K. G. Leow and S. C. Foo (National University of Singapore). This is a

CLASS-2 field experiment.


This project is disseminated as file numbers 41 (summer - HVAC) and 42 (summer -NV) in



de Dear, R. J., Leow, K. G. and S. C. Foo (1991) “Thermal comfort in the humid tropics:

Field experiments in air conditioned and naturally ventilated buildings in Singapore”.

International Journal of Biometeorology, Vol. 34, pp. 259-265.

de Dear, R.J., Leow, K. G. and A. Ameen (1991) “Thermal comfort in the equatorial climatic

zone -- Part II: Climate chamber experiments on thermal acceptability in Singapore”.

ASHRAE Transactions, Vol. 97(1), pp. 880-886.


The field experiments were conducted in both summer and winter seasons in Singapore

which is a wet equatorial climate.

Sample buildings


Sample Size (n) Climate Controls (bldgtype)

Floor Area

Occupancy Pattern

1 333 HVAC office building 2 583 NV residential building

Instruments

A hot-wire sensor was used to measure air speed. Relative humidity was measured using

an aspirated psychrometer and mercury-in-glass thermometers were used to measure air

and globe temperature. For globe temperature a 0.15m copper sphere was used.



Questionnaire

Thermal sensation was rated on the ASHRAE 7-pt scale. Thermal acceptability and thermal

preference was not addressed. Metabolic ratings were taken and clothing insulation was

estimated using the ISO7730 1984 standard. Questions of adaptive behaviour were not

considered.


Outdoor Climatological air temperature and relative humidity data at 600 hours and 1500

hours was obtained from the International Station Meteorological and Climate Summary

CDROM (ISMCS, 1992) for Paya Lebar, the closest site.


The research design was cross-sectional which satisfied the assumptions for RP-884, that

all subjects were independent. Clothing insulation estimated using the ISO7730 1984

standard was corrected to the ASHRAE55 1992 standard. 0.15 clo was added to the total

clothing ensemble insulation for the insulation effects of a chair forming a separate variable

in RP-884.



C.16. Project Title - The Steelcase building. Grand Rapids Michigan, US


F. Bauman et al. (CEDR at the University of California at Berkeley).

This is a CLASS-1 field experiment.





This project was conducted in winter in Grand Rapids, Michigan. Grand Rapids has a

continental location in the Great Lakes region of North America and has a humid mid-

latitude climate.

Sample buildings



Floor Area

Occupancy Pattern


Instruments

The Grand Rapids, Michigan field experiment was not part of the Advanced Customer

Technology Test (ACT2) study but was carried out in an identical format. A cart was set up

with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m. The sensors chosen

were selected to meet the response time and accuracy requirements of ASHRAE Standard

55-81 and ISO Standard 7730 for thermal assessment. YSI series 700 probes with vinyl-

coated tips were used to measure air temperature. Globe temperature was measured by

attaching a 38 mm diameter table tennis ball on the temperature sensors. The balls were

painted grey for correct emissivity. Air velocity was measured by Dantec 54R10

anemometers, which are omnidirectional fully temperature-compensated sensors. Dewpoint

temperature was measured by a General Eastern DEW-10 chilled mirror dewpoint

transducer. All parameters were measured at all three heights except dewpoint temperature

which was only measured at 0.6m. Radiant asymmetry and illuminance where also recorded

but were not essential to the purpose of RP-884.



Questionnaire













Outdoor Meteorological data files are for Grand Rapids, MI, USA for the period January to

February 1992 were bought from the State Climatologist for Michigan by RP-884. The files

supplied had 24 hourly Temperatures (F) and Relative Humidity (%) for the 60 day period

required, from which air temperatures and relative humidities at 600 hrs and 1500 hrs were

extracted.




Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little



then added for chair insulation. The research design of this field experiment was part

longitudinal and part cross-sectional, but for the purposes of RP-884, independence

between subjects was assumed.



C.17. Project Title - Sunset Building: a study of occupant thermal comfort in

support of PG&E’s Advanced Customer Technology Test (ACT2) for maximum

energy efficiency


Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This

is a CLASS-1 investigation.





Benton, C. C. and Brager, G. S. (1994) Sunset Building: Final Report; A study of occupant

thermal comfort in support of PG&E’s advanced customer technology test (ACT2) for

Maximum Energy Efficiency, CEDR.

Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone

Progress Report (CEDR UC Berkeley).


San Ramon is one of 3 location, in which 2 of the 4 components of the ACT2 project were

carried out. San Ramon falls within a Mediterranean climate zone, but experiences local

climatic effects due its location. San Ramon is inland east of San Francisco Bay and almost

directly east of the city of San Francisco. The field experiments were conducted across the

summer and winter months.

Sample buildings



Floor Area

Occupancy Pattern

1 152 HVAC office building 2 133 HVAC office building 3 96 HVAC office building

Instruments



A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m.

The sensors chosen were selected to meet the response time and accuracy requirements of


probes with vinyl-coated tips were used to measure air temperature. Globe temperature

was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors.

The balls were painted grey for correct emissivity. Air velocity was measured by Dantec

54R10 anemometers, which are omnidirectional fully temperature-compensated sensors.

Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror

dewpoint transducer. All parameters were measured at all three heights except dewpoint

temperature which was only measured at 0.6m. Radiant asymmetry and illuminance where

also recorded but were not essential to the purpose of RP-884.

Questionnaire

The questionnaire consisted of an on-line, laptop-computer based questionnaire, which

addressed conditions at the time physical measurements were being taken and a

background questionnaire. The latter covered subject details such as health and emotional

characteristics, office description, work area and job satisfaction, environmental sensitivity,

plus personal comfort, satisfaction and perceived control. In the on-line section thermal

sensation was rated on the 7-pt ASHRAE scale. Thermal preference was assessed on a

descriptive 3-pt scale. Thermal acceptability was not rated. Metabolic rate was estimated

based on a checklist referring to the subjects activity in the 15 minutes before completing the

on-line questionnaire, using tables in the ASHRAE Handbook of Fundamentals (HOF,

1985). Clo estimates were based on responses to the clothing item checklist provided in the

on-line questionnaire from the ASHRAE Standard 55-81 method.


Outdoor Meteorological air temperature data was obtained by request to the National

Climate Data Center (NCDC) for San Ramon and humidity was obtained from the

International Station Meteorological Climate Summary CDROM for the closest available site

(Stockton). From this data air temperatures and relative humidities at 600 hrs and 1500 hrs

were extracted for RP-884 purposes.




This project was conducted based on the format of RP-462 (RP-702). Since RP-884 itself is

based primarily on RP-702 and subsequently on RP-462 little standardisation was

necessary. However, clothing was based on the ASHRAE 55-81 method, and so required

conversion into equivalent ASHRAE 55-92 values. 0.15 clo was then added for chair

insulation. The research design of this project was longitudinal, but for RP-884 purposes all

subjects were assumed to be independent (ie. cross-sectional).



C.18. Project Title - The Verifone building, a component of the Advanced Customer

Technology Test (ACT2) project.


Charles C. Benton and Gail S. Brager (CEDR at University of California at Berkeley). This

is a CLASS-1 field experiment.




Benton, C. C. and Brager, G. S. Advanced Customer Technology Test (ACT2) Verifone

Progress Report (CEDR UC Berkeley)


This field experiment was conducted in winter in Auburn, California and is one of the

components of the ACT2 project. Auburn has a Mediterranean bordering on high altitude

climate and is located inland and to the north east of San Francisco.

Sample buildings



Floor Area

Occupancy Pattern


Instruments

A cart was set up with all sensors attached in desired positions of 0.1m, 0.6m and 1.1m.

The sensors chosen were selected to meet the response time and accuracy requirements of


probes with vinyl-coated tips were used to measure air temperature. Globe temperature

was measured by attaching a 38 mm diameter table tennis ball on the temperature sensors.

The balls were painted grey for correct emissivity. Air velocity was measured by Dantec

54R10 anemometers, which are omnidirectional fully temperature-compensated sensors.

Dewpoint temperature was measured by a General Eastern DEW-10 chilled mirror

dewpoint transducer. All parameters were measured at all three heights except dewpoint



temperature which was only measured at 0.6m. Radiant asymmetry and illuminance were

also recorded, but not essential to the purpose of RP-884.

Questionnaire













An error in dates requesting outdoor air temperature data for Auburn from the National

Climate Data Center (NCDC) resulted in the use of climatological data for both air

temperature and relative humidity at 600 hours and 1500 hours. The data was obtained

from the International Station Meteorological Climate Summary CDROM (ISMCS, 1992) for

the closest available site, Sacramento.




Since RP-884 itself is based primarily on RP-702 and subsequently on RP-462 little



then added for chair insulation. The research design of this project was longitudinal, so for

RP-884 purposes all subjects were assumed to be independent (ie. cross-sectional).


Appendix D page 285 MRL Australia

APPENDIX D - CLIMATE CLASSIFICATION



Figure D.1: The climate classification used throughout the RP-884 database.


Appendix E page 287 MRL Australia

APPENDIX E - CODEBOOK FOR RAW DATA IN RP-884 DATABASE



RP-884 Variable Coding Conventions variable's

Type of data code name Description of variable and units

Basic blcode building ID code Identifiers sub subject number

age subject's age [years] sex subject's gender [0=male, 1=female] year year

day julian date (jan 1=1, dec 31=365)

time time thermal ash ASHRAE Thermal Sensation Scale [-3, +3] questionnaire prxy_tsa Thermal acceptability defined as -1.5<=ASH<=+1.5

[1=unacc. 2=acc] tsa Thermal Acceptability Question [1=unacc. 2=acc] mci Thermal Preference [1=want cooler, 2=no change,

3=want warmer] vent air movement acceptability [6(very acc), 1(very unacc)] avm air movement preference [3(more), 2(no change),

1(less)] comf General thermal comfort right now [1=very uncomf,

6=very comf] act10 metabolic activity in last 10 minutes [met] act20 metabolic activity between 20 and 10 minutes ago [met] act30 metabolic activity between 30 and 20 minutes ago [met] act60 metabolic activity between 60 and 30 minutes ago [met] met average metabolic rate of subject [met] clo ensemble clothing insulation [clo] upholst insulation of the subject's chair [clo] insul clothing plus upholstery insulation [clo]

Indoor Climate ta_h air temperature at 1.1m above floor [oC] Physical Obs ta_m air temperature at 0.6m above floor [oC]

ta_l air temperature at 0.1m above floor [oC] dewpt dewpoint temperature [oC] prta_b plane radiant asymmetry temperature [oC] tg_h globe temperature at 1.1m above floor [oC] tg_m globe temperature at 0.6m above floor [oC] tg_l globe temperature at 0.1m above floor [oC] vel_h air speed 1.1m [m/s] vel_m air speed 0.6m [m/s] vel_l air speed 0.1m [m/s] turb_h turbulence intensity at 1.1m above floor [frac] turb_m turbulence intensity at 0.6m above floor [frac] turb_l turbulence intensity at 0.1m above floor [frac]

Continue Table.



variable's Type of data code name Description of variable and units

calculated taav average of three heights' air temperature [oC] indices trav average of three heights' mean radiant temperature [oC]

top average of TAAV and TRAV (operative temperature) [oC]

velav average of three heights' air speed [m/s] velmax maximum of three heights' air speeds [m/s] tuav average of three heights' turbulence [frac] pa vapor pressure [kPa] rh relative humidity [%] et new effective temperature index et* [oC] set new standard effective temperature index set* [oC] tsens two-node tsens index [-1.5, +2.0] disc two-node disc index [-4, +4] pmv Predicted Mean Vote, Fanger's Model [-3, +3] ppd Predicted Percentage Dissatisfied, Fanger's Model

[frac] pd_h Percent Dissatisfied due to Draft at 1.1m height, Fanger

et al [frac] pd_m Percent Dissatisfied due to Draft at 0.6m height, Fanger

et al [frac] pd_l Percent Dissatisfied due to Draft at 0.1m height, Fanger

et al [frac] pd_max Percent Dissatisfied due to Draft, max of all 3 heights,

Fanger et al [frac]

personal PCC perceived control over thermal environ [1=no control, 5=complete control]

environmental PCC_AG aggregate perceived control from PCEC1...PCEC7 control PCS how satisfied are you with PCC [1=very dissat, 6=very

sat] PCEC1 can you open/close windows? [1=yes, 0=no] PCEC2 can you open/close external doors [1=yes, 0=no] PCEC3 can you open/close internal doors [1=yes, 0=no] PCEC4 can you adjust thermostats [1=yes, 0=no] PCEC5 can you adjust curtains/blinds [1=yes, 0=no] PCEC6 can you adjust local heaters [1=yes, 0=no] PCEC7 can you adjust local fans [1=yes, 0=no]

Do you exercise any PCED1 windows [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

of these options? PCED2 external door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

PCED3 internal door [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

PCED4 thermostat [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

PCED5 curtains/blinds [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

PCED6 local heater [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]

PCED7 local fan [1=na, 2=never, 3=rarely, 4=sometimes, 5=often, 6=always]



Continue Table. variable's

Type of data code name Description of variable and units

Outdoor Meteorol day15_ta outdoor 3pm (max) air temp on day of survey [oC] Observations day06_ta outdoor 6am (min) air temp on day of survey [oC]

dayav_ta outdoor average of min/max air temp on day of survey [oC]

day15_rh outdoor 3pm (min) rel humid on day of survey [%] day06_rh outdoor 6am (max) rel humid on day of survey [%] dayav_rh outdoor average min/max rel humid on day of survey [%] day15_et outdoor 3pm ET* on day of survey (Ta and rh at time of

daymx_ta) [oC] day06_et outdoor ET* on day of survey (Ta and rh at time of

daymn_ta) [oC] dayav_et outdoor average of min/max ET* on day of survey [oC]


Appendix F page 291 MRL Australia

APPENDIX F - CODEBOOK FOR THE RP-884 META ANALYSIS



Meta Analysis Codebook Variable Description

authors investigators of the study season season of study country country study carried out in

city city where the study was done seasnum 1 = summer (cooling season), 2 = winter (heating season) dataclas grade of research methods and resulting data (1st, 2nd, 3rd) bldgtype type of building, ie. 1 = climate controlled (HVAC), 2 = free running (NV) and 3

= mixed blcode individual building code

n sample size per building m_taav mean taav s_taav standard deviation taav m_trav mean trav s_trav standard deviation trav m_top mean top s_top standard deviation top

m_velav mean velav s_velav standard deviation velav m_rh mean relative humidity s_rh standard deviation relative humidity m_et mean et* s_et standard deviation of et*

ASH55_92 % indoor climatic obs falling within the relevant ASHRAE 55-92 comfort zone predneut the predicted neutral operative temperature given conditions of vel, rh, insul and

met. deltneut neut_top minus predneut preftemp defined in terms of operative temperature by probit analysis of MCI discrep neut_top minus preftemp m_set mean set* s_set standard deviation set*

m_pmv mean pmv s_pmv standard deviation pmv m_ppd mean ppd s_ppd standard deviation ppd

mpd_max mean pd_max (pd_max being the largest PD of the three heights measured) spd_max standard deviation pd_max m_ash mean ashrae thermal sensation vote s_ash standard deviation ashrae thermal sensation vote m_met mean metabolic rate (met) s_met standard deviation metabolic rate (met)

m_insul mean value of the summed clothing and chair insulation (clo) s_insul standard deviation of the summed clothing and chair insulation (clo) m_clo mean clothing insulation (clo) s_clo standard deviation of clothing insulation (clo)

mpcc_ag mean pcc_ag (pcc_ag is the index of perceived control) spcc_ag standard deviation pcc_ag (pcc_ag is the index of perceived control)



Continue Table Variable Description

mday15ta mean day15_ta (daily maximum outdoor air temperature degC) sday15ta standard deviation day15_ta (daily maximum outdoor air temperature degC) mday06ta mean day06_ta (daily minimum outdoor temperature degC) sday06ta standard deviation day06_ta (daily minimum outdoor temperature degC) mdayavta mean dayav_ta (mean of daily min and max temperatures degC) sdayavta standard deviation dayav_ta (mean of daily min and max temperatures degC) mday15et mean day15_et (ET* at time of max outdoor air temperature degC) sday15et standard deviation day15_et mday06et mean day06_et sday06et standard deviation day06_et mdayavet mean dayav_et sdayavet standard deviation dayav_et f_mci_2 % frequency when mci = 2 (no change) f_tsa_2 % frequency when tsa = 2 (acceptable) fprxysat % frequency when prxy_tsa = 2 (-1.5<ASH<+1.5) assumed acceptable grad_top gradient of the regression model mean_ash verses dose_top (ashtop)

p_top p value of the regression model testing gradient coefficient = 0 neut_top neutrality for dose_top (mean_ash = 0 in regression model ashtop) rang_top the acceptibility range for dose_top (mean_ash = 1.5 - mean_ash = -1.5 in regr

model ashtop). grad_et gradient of the regression model mean_ash verses dose_et (ashet)

p_et p value of the regression model testing gradient coefficient = 0 neut_et neutrality for dose_et (mean_ash = 0 in regression model ashtop) rang_et the acceptibility range for dose_et (mean_ash = 1.5 - mean_ash = -1.5 in regr model

ashtop). grad_set gradient of the regression model mean_ash verses dose_set (ashset)

p_set p value of the regression model testing gradient coefficient = 0 neut_set neutrality for dose_set (mean_ash = 0 in regression model ashset) rang_set the acceptibility range for dose_set (mean_ash = 1.5 - mean_ash = -1.5 in regr

model ashset) grad_pmv gradient of the regression model mean_ash verses dose_pmv (ashpmv)

p_pmv p value of the regression model testing gradient coefficient = 0 neut_pmv neutrality for dose_pmv (mean_ash = 0 in regression model ashpmv) rang_pmv the acceptibility range for dose_pmv (mean_ash = 1.5 - mean_ash = -1.5 in regr

model ashpmv)


Appendix G page 295 MRL Australia

APPENDIX G - AULICIEMS’ ADAPTIVE MODEL DATABASE



indoor TOP

outdoor temp

neutrality Location active/passive climate controls

Researcher (see Auliciems 1981b)

22.8 19.8 22.7 Melbourne A Ballantyne 20.7 9.5 21.3 Melbourne A Ballantyne 20.4 11.1 20.5 Melbourne A Auliciems 1977

15.2 23.9 Sydney A Hindmarsh 13.3 22.3 Sydney A Hindmarsh 21.6 24.2 Sydney P Hindmarsh 19.4 21.4 Sydney P Hindmarsh 12.4 21 Sydney A Wong 21.3 23 Sydney A Wong

19.5 12.8 20.6 Adelaide A Auliciems 22.6 17.1 23.1 Brisbane P Auliciems 19.6 14.7 21.9 Perth A Auliciems 22.4 8.3 21.3 Armidale A Auliciems

28.1 26.2 Darwin P Macpherson 28.1 27.6 Darwin P Macpherson 28.9 26.2 Weipa P Wyndham

28.3 27.8 25.4 Pt Moresby P Ballantyne 25.9 25.4 Pt Moresby P Ballantyne

26.9 25 Pt Moresby P Ballantyne 26.9 27.2 Pt Moresby P Ballantyne

27.8 27 27.5 Honiara P Woolard 28.2 28.9 26.1 Singapore P Ellis 28.6 27 26.1 Singapore P Ellis 28.8 27 27.3 Singapore P Webb 33.4 33.5 30.1 New Delhi P Nicol 30.3 26.4 26.1 Calcutta P Rao 35.9 33.9 31.2 Baghdad P Nicol 28.8 24.8 25.8 Rio de Janeiro P Sa 24.7 21.3 24.6 Rio de Janeiro P Sa

22 22.5 Toronto A Tasker 22.8 23.9 New York A Gagge 21.5 23.6 Minneapolis A Newton

23.5 12.5 24.4 Portland A Pepler 23.6 12.5 22.1 Portland A Pepler 21.1 3.5 19.8 Swedish Towns A SIB 24.1 10.2 19 Swedish Towns A SIB 23.9 0.6 21.5 Zurich A Wanner 23.5 18.3 23.1 Zurich A Wanner 22.7 2.2 20.9 Zurich A Grandjean 23.2 17.8 21.3 Zurich/Basel/Bern A/P Grandjean 18.1 4.7 18.4 London A Bedford 18.8 6.7 19.2 London A Black 19 17 22.2 London A Black

17.2 5.2 17.5 London A Fox 20.5 10.6 22.4 London A Wyon 19.7 4.7 18.9 London A Angus 21.4 15.9 19.4 London P Hickish 21.1 17.9 21.3 Garston A Humphreys 21.4 3.8 19.9 Garston A Humphreys 21.4 7.7 19.7 Garston A Humphreys 21.4 10.8 19.3 Garston A Humphreys 21.4 14.4 20 Garston P Humphreys 21.4 16.4 20.2 Garston P Humphreys

developing an adaptive model of thermal comfort …...i developing an adaptive model of thermal...

Documents