Proceedings of 8th

Windsor Conference: Counting the Cost of Comfort in a changing

world Cumberland Lodge, Windsor, UK, 10-13 April 2014. London: Network for

Comfort and Energy Use in Buildings, http://nceub.org.uk

Challenges in designing for comfort – Comfort and energy use

characterization in residential apartments

E. Rajasekar, R. Soumya, Rajan Venkateswaran

Center for Excellence and Futuristic Developments, B&F (IC), L&T Construction,

India

Abstract

This article presents the results of a thermal comfort investigation carried out in a residential gated

community located in a hot-humid climate. The study comprises of real-time field monitoring of

thermal comfort in representative apartment units and assessment of the utility and cooling energy

consumption in these residences. Utility energy consumption data of the residences for one year period

was obtained and a survey was administered to identify the trend of air-conditioner use. The results are

summarized and used to validate a simulation model. The pattern of comfort and energy use variation

across the gated community was analysed. The variation in cooling energy consumption and its

relevance to the discomfort severity across residences was analysed. This article presents the

challenges in ensuring optimal thermal comfort for all units in such buildings and discusses the

possible commercial value of thermal comfort.

Keywords: Thermal comfort, residential buildings, adaptive criteria, cooling energy, field studies

1 Introduction

In naturally ventilated buildings, indoor thermal comfort is determined by a multitude

of factors which includes design configurations, envelope characteristics and outside

boundary conditions. The physical indoor thermal conditions have been found to

relate strongly with the ambient thermal stimuli as observed by Kruger & Givoni

(2004, 2008, 2011), Shastry et al (2012) and Udaykumar et al (2013). Apart from the

physical parameters, the comfort perception and thermal expectations are known to be

driven by psychological factors. These aspects remained the focal point of studies

reported by Humphreys (1981), Brager & de Dear (1998), Humphreys & Nicol (1998),

Nicol (2004), Indraganti (2010) and Rajasekar & Ramachandraiah (2010). It can be

postulated that if these physical and psychological stimuli are appropriately tuned,

thermal comfort can be achieved to a greater extent of the occupied duration without

resorting to mechanical condtioning. It is evident from Humphreys & Nicol (1998)

that if the adaptive processes are working satisfactorily, the kind of temperatures and

other thermal parameters in the buildings they are living in should have suited their

requirements.

The energy consumption for comfort conditioning can be theoretically equated to the

magnitude of indoor thermal discomfort. Abreu et al. (2010) found that

approximately 80% of household electricity use can be explained within the two

patterns of persistent daily routines and patterns of consumption or baselines typical

of specific weather and daily conditions. Santin et al. (2009) observed that occupant

characteristics and behaviour influences energy use by 4% while building

characteristics influence energy use by 42%. However the authors noted that some

occupant behaviour is determined by the type of dwelling or HVAC systems and,

therefore, the effect of occupant characteristics might be larger than expected.

Especially in the case of apartments which accommodate a number of typical

residences with varying outside boundary conditions in every floor plate, the

effectiveness of adaptive process is perhaps bound by the constraints that may vary

widely from one residence to the other. One of the recent studies (Khaled el-deeb et

al., 2012) showed that common building forms and urban patterns do not always yield

the expected reduction of energy consumption.

In India, a substantial share of urban residential settlement is catered condominium

style gated communities. Given the trend of depleting renewable energy sources, the

aspects of thermal discomfort severity and energy relating to comfort conditioning

have increasingly become the focal points in the residential building sector. A study

conducted by Viginie & Michael (2007) shows that in India, on an average the

household per-capita energy consumption grow at the rate of 8.2% a year and relative

to 2013, the consumption will be about 4 times higher by 2030. In this context, the

present study deals with thermal comfort and energy consumption pattern across eight

condominiums in a gated community. The objectives of the study are (i) to

investigate the thermal comfort characteristics of residential units in condominiums

and to analyse the factors that influence it (ii) to study in real-time, the variations in

actual and simulated cooling energy use pattern and to investigate the viability of

different methods for improving comfort in such residential developments.

2 Details of the study

Chennai (13oN, 80.3

oE) located in the east cost of India represents a typical hot humid

climate. The study pertains to a gated community encompassing 650 residential units

distributed in 6 apartment blocks which are 14 floors high. There are 3 apartment

types; type I, type II and type III with floor areas 120 sq. m., 160 sq. m. and 186 sq. m.

respectively (fig 1(a)).

Figure 1. (a) Site layout (b) Typical floor plan of type III apartment block

Based on a pilot study and a climate and sun-path analysis, a field monitoring setup

was established in three residential units located at the 13th

floor and continuous

measurements were made for one year duration. The present study focuses on type III

apartments which are termed premium 3-bedroom units catering to higher middle

income group of people (fig 1(b)). They enclose three bedrooms – the master

bedroom (MBR, 21 sq. m), kids’ bedroom (KBR, 15 sq. m) and guest bedroom (GBR,

11 sq. m) – living-cum-dining area (33sq.m.) and kitchen (13.5 sq.m.). The envelope

is made of 150 mm thick reinforced concrete wall (U-value of 3.77 W/m2K) with

smooth plastered white finish on both the sides (absorptivity of 0.3). Calculated heat

capacity of the envelope is 86.4 Wh/m2C and thermal time constant is 9.7 hours.

3 Real-time measurements and Utility power consumption

Indoor thermal comfort variations were recorded using 2 numbers of Delta OHM

thermal comfort meters (Accuracy ±0.35ºC, ±2.5% RH, ±0.05m/s for 0-1m/s air

velocity and ±0.15m/s for 1-5m/s air velocity), 2 numbers of Delta OHM 16 channel

data loggers with T-type thermocouples (accuracy ±0.5ºC), 8 numbers of Supco LTH

– Temperature, RH loggers (accuracy ±1ºC, ±2% RH ) and 3 numbers of Supco LT2

– Temperature loggers (accuracy ±1ºC). Using this measurement setup indoor air

temperature (Tin), mean radiant temperature (Tr), relative humidity (RH), air velocity

(Va), inside and outside surface temperature of wall (Ts) were recorded at 10 minutes

interval.

Figure 2.(a) Typical indoor instrument setup (b) Mini Weather station

From the measured thermal parameters, comfort in terms of Fanger’s predicted mean

vote (PMV) was estimated for a clothing value (clo) of 0.8 and metabolic rate of 1.2.

In addition two heat flux plates (accuracy 43 µV/W/m2) connected with LSI Lastem

data logging system were used for envelope heat flow measurements. Outdoor

monitoring setup - a watchdog mini-weather station (installed at 3 m height above the

14th

storey) recorded air temperature, relative humidity, wind velocity and direction

and rainfall intensity. These measurements were made for one year duration in three

un-occupied residential units. Fig. 2 shows some of the snap shots of the

measurement setup. In addition, Tin and RH data were collected from 10 occupied

residences using Supco LTH – Temperature, RH loggers for comparative analysis.

Utility power consumption over a period of 2 years was obtained for all the occupied

residences in the gated community from the electricity regulatory authority. A

subjective survey on utility power consumption was administered with representative

condominiums (sample size of 100 units) regarding the capacity and usage pattern of

air-conditioners and other major electrical equipment. Data on the lighting fixtures

and their lighting power densities were also collected. The questionnaire used for this

purpose is presented as appendix A. Fig. 3 and 4 provide the details and pattern of

air-conditioner operation obtained through the subjective surveys. Peak operation of

air-conditioner was found to be during the months of May, June and July (summer).

This study hence focused on the thermal discomfort and cooling energy consumption

pattern pertaining to the summer months. A large non-uniformity in the pattern of air-

conditioner usage and set-point temperatures were evident from the surveys. The

details of it are depicted in the form of a histogram in figs. 3(a) and (b). The data

obtained relating to occupancy levels per residential unit (µ=3.5), set temperature

(µ=23.5oC), occupancy pattern and hours of air-conditioner operation (µ=6 hours)

were considered as inputs for the simulation studies discussed in section 5.

Fig 3(a). Preferred set temperature (b) Duration of air-conditioner usage

Typically, the type III units had three of the bedrooms air-conditioned. The efficiency

of the system (energy efficiency ratio, EER) was found to vary among the residences

as summarized in fig. 4(a). Fig. 4(b) shows the statistical summary of electricity

consumption of type III apartments for a period of two years (2012 – 2013) summer

(May), winter (December) and non-peak months (March, September).

Figure 4(a). Star rating of air conditioners (b) Summary of electricity consumption in block type III

Among the 224 numbers of type III units being considered, 170 units had consistent

occupancy for the two year duration being considered. The remaining units had

remained partly or fully unoccupied for the duration and hence were not considered

for the consumption analysis. Similarly, a normalization of the electricity

consumption data was carried out to identify the outliers. Outliers in this case include

a few residential units (5 nos.) which had their living rooms air-conditioned in

addition to the three bedrooms. It also includes about 12 residential units for which

only the lighting loads were reflected in the power consumption during summer

months. This brought the sample size of the study to 153 residential units.

4 Results from real-time monitoring

4.1 Ambient micro-climate

Daily maximum outdoor dry-bulb temperature (Tout) varied between 38oC and 42

oC

during peak summer (May) and daily minimum Tout varied between 22oC to 25

o

during winter. Fig. 5 shows the distribution of hourly temperature and RH variations

during summer (May) and winter (December). The hatched boundary indicates

comfort zone prescribed by the national building code, India. Average diurnal

2 3 4

variation of 10oC during summer and 7

oC during winter was noted in the on-site

measurements as shown in the figure. By virtue of its vicinity to the sea shore (4 km

from Bay of Bengal) and its suburban location with very low development density,

the site recorded air velocities up to 6 m/s especially in the evenings.

Figure 5. Psychometric chart for summer and winter

4.2 Indoor thermal variations

Indoor thermal variations were investigated in terms of Tin, Tr, RH and Va for

different occupied zones and Ts and heat flux of various wall and roof surfaces. On a

typical summer day, Tin exhibited a diurnal variation of 4 to 5oC as compared to the

Tout variation of about 10oC. Thermal lag between Tout and Tin ranged from 1 to 2.5

hours depending on orientation, design configurations and window sizes of the zone.

The trend in Tr variations followed that of Tin. Maximum daily Va of up to 1.5 m/s

were recorded and the average values range from 0.3 to 0.6. Fig 6(a) and (b) show the

Tin, Tr and Va for a west exposed room for 3 representative days in summer (May 8 to

10). East and west exposed walls experienced higher heat gains. Maximum daily heat

gains varied from 15 to 20 W/m2 and heat losses varied from 5 to 8 W/m

2.

Figure 6(a). Indoor thermal variations (b) Ts(in) and heat flux variations

4.3 Comfort estimate based on adaptive comfort criteria

Adaptive thermal comfort was evaluated in terms of the running mean temperature

(Trm), adapted from Nicol & Humphreys (2010). Thermo-neutrality and acceptable

limits were adapted from the findings of Rajasekar & Ramachandraiah (2010) as

shown in equation 1.

Eq.1

The upper and lower limits are adapted based on EN15251 recommendations for

acceptability category I and II which represents ‘high expectation’ and ‘normal

expectation’ respectively.

Figure 7. Comfort evaluation based on Trm

Fig. 7 shows the hourly Tin variation and the Trm acceptability limits estimated based

on measured Tout for one year duration. Monthly summary of RH variations have also

been presented. The graph corresponds to the measurements made in KBR which had

an east and north exposure. Based on this analysis the frequency for which Tin

exceeded Tn (of the corresponding day estimated based on Trm) was estimated. Table

1 presents a summary for KBR and MBR.

Table 1: Frequency of thermal discomfort estimated based on Trm

Months

% time Tin exceeds Tn (based on Trm) Base Neutral

Temperature High expectation Normal expectation

KBR MBR KBR MBR KBR MBR

April 3% 0% 0% 0% 73% 61%

May 32% 46% 20% 26% 87% 89%

June 30% 32% 15% 17% 85% 79%

July 4% 6% 0% 0% 57% 48%

5. Simulation studies

In order to analyse the variations in comfort and corresponding cooling energy use

across the gated community, a simulation model was developed in Design Builder

software tool. The model was then exported to Energy Plus V8.1 for carrying out

parametric simulations. The actual design configurations and envelope properties

were adopted for the simulation model and the weather data was obtained from

ISHRAE database (ISHRAE weather data 2012). Fig. 8 shows a screen shot of the

site plan and typical floor plan model developed in Design Builder software tool.

Simulations were carried out (1) for a naturally ventilated scenario for estimating the

magnitude of thermal discomfort and (2) a typical intermittently cooled scenario for

estimating the cooling energy demand. The air-conditioning system operation

schedule and set-point temperatures for the simulations were based on the subjective

survey results. A typical lower floor (1st floor) and an upper floor (13

th floor) were

simulated for the above mentioned conditions. The results from the simulations were

compared with the real-time samples in order to verify the consistency and the pattern

of variation. The methodology adopted is shown in fig. 9.

Figure 8. Design builder model of the site and a typical floor plan

Fig. 9. Methodology adopted for the simulation analysis

5.1 Comparison of measured and simulated results

During the days when measured ambient weather conditions and simulation weather

data were similar, the measured and simulated indoor thermal comfort results were

found to be in good agreement. Fig. 10(a) compares one instance of measured and

simulated indoor air temperature (Tin) during peak summer (May 7 to 10). During

this period only a marginal variation existed between actual and simulated ambient

weather conditions. Similar results were obtained in the corresponding PMV

variations between measured and simulated instances. For the purpose of consistency

and to leave scope for further studies beyond the context of this article, the results for

further discussions have been based on the ISHRAE weather data. A comparison of

the simulated and actual power consumption was made in which the actual number

and type of air conditioners, set temperatures and operation pattern for a few

residences from the subjective survey data were simulated. The pattern of power

consumption was similar in the simulated and actual scenario as indicated in fig. 10(b).

Figure 10. Comparison of measured and simulated values (a) Tin (b) cooling energy consumption

Based on the findings presented in figs. 3 and 4, the simulations were carried out

considering a cooling set point temperature of 23.5oC and operational duration of 6.5

hours per day. An energy efficiency ratio (EER) of 3.0 W/W was considered for the

cooling system.

6 Results and Discussion

Severity of thermal discomfort was calculated in terms of degree discomfort hours

(DDH) given by

∑

and expressed in degree hours. In the residential setting concerning the present study,

the cooling energy consumption was primarily from the night-time conditioning.

Considering this fact, DDH is calculated for the duration 09:00 PM to 6:00 AM.

6.1 Inter-zonal variations in discomfort and cooling energy demand

This section presents the variations in DDH and cooling energy demand among the

conditioned zones within a residential unit. Fig. 11(a) presents a binned correlation of

daily Tout maxima and the corresponding night time DDH for the three bedrooms of

unit 4 in block A. KBR is exposed to east and north, GBR to north and west and

MBR to west orientation. The data pertains to summer (March – July) where heat

discomfort and comfort cooling were predominant. Night time DDH exhibited a

strong and linear correlation with the ambient daily Tout average irrespective of design

configurations. Thermal discomfort in MBR was higher than the other two zones.

Though the difference is marginal in the trend lines, the magnitude and frequency

indicated by the bin sizes are different from each other. Corresponding variation in

the cooling energy consumption is shown in fig. 11(b). In order to account for the

floor area variations among the zones, the area averaged cooling energy consumption

(measured in KWh/m2) has been considered. In conjunction with the DDH variations,

cooling energy consumption in MBR was found to be considerably higher than that of

KBR. Similar variations were also noted in other residential units.

Figure 11(a). Trend in DDH variation (b) Trend in cooling energy consumption

6.2 Variations between units

The floor area and design configuration are identical across the residential units of

type III apartment block. However these units vary from each other in terms of

orientation and solar exposure due to which the DDH and corresponding cooling

energy consumption were found to vary from one another. To analyse the differences

in thermal discomfort variations across residential units, DDH and the corresponding

cooling energy consumption for the three bedrooms were averaged for each unit.

Table 2 shows the cross correlation results of DDH for 8 residential units, 4 each in

apartment block A and B.

Table 2: Inter-Item Correlation Matrix of DDH in Block A and B

A 1 A 2 A 3 A 4 B 1 B 2 B 3 B 4

A 1 1.00 0.59 0.32 0.67 0.99 0.55 0.35 0.64

A 2 0.59 1.00 0.54 0.99 0.59 0.99 0.53 0.99

A 3 0.32 0.54 1.00 0.48 0.35 0.65 0.99 0.52

A 4 0.69 0.99 0.49 1.00 0.69 0.96 0.49 0.99

B 1 0.99 0.59 0.35 0.69 1.00 0.56 0.39 0.64

B 2 0.55 0.98 0.65 0.96 0.56 1.00 0.64 0.98

B 3 0.35 0.53 0.99 0.49 0.39 0.64 1.00 0.53

Thermal discomfort was more uniform for residential units with similar solar

exposure conditions. For instance, the strength of correlation between A1-B1, A2-B2,

A3-B3 and A4-B4 were relatively stronger when compared to the strength of

correlation within units of block A and B. A similar trend in variation was observed

in terms of cooling energy. Fig. 12 shows the cooling energy demand variations in

the four residential units of block A. Unit 4 with the longer axis exposed to east and

west orientations experienced higher cooling energy demand compared to other units.

Similarly, Unit 1 with its longer axis exposed to north and south experienced a lower

cooling energy demand compared to the other units.

Figure 12. Trend in cooling energy demand for Block A

The significance of variation among residential units located at the same floor level of

a given block was tested through a one-way between subjects ANOVA test. The

daily total cooling energy demands for the four units located in the upper floor level

(level 13) of block A, obtained through simulations were considered for the analysis.

There was a significant difference among the sample means of the four units at

p<0.05 level (F3,480=26.8, p=0). Post hoc comparison using the Tykey HSD test

indicated that the mean score for units with longer axis exposed to similar orientations

were not significantly different from each other. For instance, the mean cooling

energy demand for unit 1 (µ=24.2, σ=4.8) was not significantly different from unit 3

(µ=24.6, σ=4.9). On the other hand, the cooling energy demand for units with

varying longer axis orientations were significantly different, such as unit 2 (µ=28.4,

σ=5.7) and unit 3 (µ=24.6, σ=4.9).

6.3 Variation between blocks

By virtue of the site planning and the solar altitude and azimuthal angles the

apartment blocks were found to mutually shade each other. This phenomenon

resulted in dissimilarity in the insolation levels of walls between lower and upper

floor levels of the apartment blocks (fig. 13a). The result has been obtained from

mutual shading and insolation analysis carried out using Autodesk Ecotect software

tool. Fig. 13 (b) shows the annual shadow pattern cast by the apartment blocks in

which the extent of mutual shading can be visualized.

Figure 13(a). Insolation on wall surfaces - summer (b) Annual shadow pattern

Figure 14(a). Cooling energy demand -Unit 3 (b) Cooling energy demand -Unit 1

Fig. 14 (a) and (b) compares the variation in cooling energy consumption for units 1

and 3 from block A and B located in the lower floor level. The cooling energy

demand of unit 3 in block A was found to be marginally higher than that of unit 3 in

block B, which is shaded by the adjacent block. Similarly, cooling energy demand of

unit 1 in block B was found to be marginally higher than that of unit 1 in block A.

The effect of mutual shading of adjacent blocks on the cooling energy demand was

tested through a two-way ANOVA test. The simulated daily total cooling energy

demands of these four units at lower and upper floor levels were compared. At the

lower floor level, there was a significant difference among population means of the

above cases at p<0.05 level (F3,480=31.4, p=0). Post hoc comparison using the Tukey

HSD test indicated that the mean score for unit 3 in block A (µ=25, σ=5.4) was

significantly different than that of unit 3 in block B (µ=28.9, σ=6.4). Similarly the

mean score for unit 1 in block A (µ= 24.2, σ= 5.2) was found to be significantly

different than that of unit 1 in block B (µ=30.4, σ=6.4). At the upper floor level, there

was no significant difference among population means of the above cases.

6.4 Floor level variations in discomfort and cooling energy demand

Fig. 15 compares the average cooling energy demand for residential units located in

the lower and upper floor level. The average demand in the upper floor level was

found to be marginally higher than the lower floor level.

A one-way between subjects ANOVA was conducted to compare the effect of floor

height variation on cooling energy demand in residential units located at lower and

upper floor levels. The daily average cooling energy demands for the four units were

considered for the analysis. There was a significant effect of floor height on the

cooling energy demand at p<0.05 level for the two cases (F1,960=55.5, p=0). Post hoc

comparison using the Tukey HSD test indicated that the mean score for the cooling

energy demand in the lower floor level (µ=26.7, σ=5.8) was significantly different

from that on the upper floor (µ=28.9, σ=7.0).

Figure 15. Cooling demand variations – lower and upper floor levels

6.5 Comparison of simulated and actual energy consumption trends Fig. 16(a) shows the overall trend in cooling energy consumption across the

residential units as obtained from the simulation studies. Maximum cooling energy is

consumed by unit 4 followed by unit 2 in all the four blocks. The residential unit

which consumes minimum cooling energy was found to vary among blocks. The

units facing the core (shaded by the adjacent block in east or west) exhibit lesser

consumption compared to those facing the periphery.

Figure 16. Energy consumption pattern (a) simulation studies (b) Actual consumption data

Fig. 16(b) shows the pattern of power consumption obtained from the actual utility

electricity consumption data. Unit 4 in all the four blocks was found to consume

higher energy compared to the other units. The pattern of consumption was found to

be non-uniform in the other orientations. The role of user preferences towards

thermal comfort and life style on the pattern of actual energy consumption was clearly

noticeable from the above results. This was found to overshadow the effect of

climatic factors and design configurations in some of the instances. It must be noted

that, though adaptive opportunities can be integrated in the built form, various

(a) Cooling energy consumption ranking

based on simulation study

(b) Cooling energy consumption based on actual

electricity consumption data

Consumption

Rank

(Decreasing order)

1

2

3

4

concerns arise in such gated communities on their effective utilization. For instance,

as shown in fig. 14, the mutual shading of blocks resulted in consumption variation

which was not noticeable in the actual scenario. This can be attributed to factors

including concerns of visual privacy apart from variations in comfort perception and

living patterns.

7 Opportunities for comfort and energy efficiency in gated communities

The non-uniformity in thermal discomfort and consequent variations in energy bills

that the residents would pay for the rest of their occupancy tenure stimulates the

following opportunities.

7.1 Design opportunities This involves customized design improvements from the comfort and energy point of

view at the residential unit level so as to maintain uniformity among units. Several

findings on influence of design factors on comfort improvements have been reported

earlier. Significant studies include those of Givoni (1994) who has discussed the

effects of building design features such as the layout, window orientation, shading and

ventilation, on the indoor environment and energy use. Suresh et al (2011) have

presented a comprehensive review of building envelope components and related

energy savings.

7.2 Adaptive opportunities and occupant inclination Occupants can be educated regarding the adaptive opportunities provided in the

design and related benefits in terms of comfort and energy expenses which could act

as stimuli for enhancing adaptive mechanism. Becker et al (1981) reported the

influence of attitudinal factors influencing residential energy use. Wilson &

Dowlatabadi (2007) discussed the influence of conventional and behavioural

economics, technology adoption theory and attitude-based decision making, social

and environmental psychology, and sociology on the decision making with specific

application to residential energy use. Occupant adaptation and scope for comfort

improvements have been reported in detail by Zain et al (2007), Ren et al (2011) and

Deuble & de Dear (2014). Studies conducted for a similar residential type in a hot-

dry climate (Rajasekar et al 2014) showed that adaptive occupancy patterns could

effect a significant improvement in indoor thermal comfort.

In order to quantify the design and adaptive opportunities, a prototype matrix was

developed which evaluates the inter-zonal thermal severity within a unit as well as

across units based on the predicted comfort and the usage pattern of the residents.

The methodology for evaluating the discomfort severity involved the following steps

Step 1. Cooling energy consumption per unit floor area of conditioned zones were

obtained through simulations

Step 2. A baseline criteria zone which consumes minimum cooling energy among the

zones was selected

Step 3. Energy consumption of the other zones were factored using this baseline and

a relative consumption was obtained

Step 4. A usage factor which depicts the magnitude to which the zone is utilized was

defined. This was assigned based on the proposed activity in the space

Step 5. Based on the relative consumption discussed in step 3 and the usage factor,

magnitude of severity was estimated. This indicates the magnitude of

thermal discomfort and related cooling energy consumption.

Step 6. Whole-house severity was estimated by averaging the zone wise severity of a

given residential unit

Step 7. Depending on the magnitude of whole-house severity, a ranking was assigned

for different units under consideration

Table 3: Thermal severity ranking matrix

Unit 1 Unit 2 Unit 3 Unit 4

Z1 Z2 Z3 Z1 Z2 Z3 Z1 Z2 Z3 Z1 Z2 Z3

Relative

consumption -0.50 0.00 -0.80 -0.63 -0.39 -0.60 -0.23 -0.22 -0.91 -0.71 -0.37 -0.68

Zoning order** 1 2 3 1 2 3 1 2 3 1 2 3

Conditioning Y Y Y Y Y Y Y Y Y Y Y Y

Relative Severity -0.50 0.00 -0.40 -0.63 -0.31 -0.30 -0.23 -0.18 -0.45 -0.71 -0.29 -0.34

Whole-house

Severity -0.45 -0.41 -0.29 -0.45

Whole-house

Ranking 3 2 1 3

** 1 – Master Bedroom (factored as 1); 2 – Kid’s Bedroom (factored as 0.8);

3 – Guest Bedroom (factored as 0.5)

Table 3 presents the matrix which analyses the three bedrooms across four residential

units of block A. For the purpose of analysis flexibility, the three rooms have been

referred as zones 1, 2 and 3 respectively (Z1, Z2 and Z3). For these zones, cooling

energy per unit floor area was obtained through the simulations. Among the 12 zones

being considered, Z2 in unit 1 was found to consume lesser cooling energy and was

considered as a base line. Consumptions of the other zones were factored with this

base line and the relative consumption is presented in row 3. Higher the negative

number, higher is the energy consumption compared to the base line. The usage

factor discussed in step 4 is listed in row 4 and is denoted as zoning order. This has

been defined as per existing zoning configuration and all the zones are assumed to be

conditioned in this case.

As per the subjective study results during summer MBR remains occupied and

conditioned for a maximum duration followed by KBR and GBR. In view of this

MBR was factored with 1.0, KBR with 0.8 and GBR with 0.5. These factors can be

varied depending on contextual variations. Relative severity was obtained by

factoring the relative consumption with the zoning order (row 6). For instance, zone 3

in unit 3 which had the highest energy consumption (-0.91) has been scaled down

since it has been zoned as guest bedroom in the existing design and was expected to

be used only intermittently. This step (step 5) opens up venues for designers to

reconfigure the zoning based on the inherent thermal severity and usage requirements.

In addition it provides stimuli for the residents to look towards modifications in living

patterns for improved comfort and energy efficiency. Whole-house thermal severity

and rank ordering were estimated based on step 6 and 7. This method provides a

commercial perspective for comfort and energy efficiency in such modular building

types and would bring more transparency on the cost of thermal comfort.

7.3 Cost interventions and challenges in implementation A life cycle cost (LCC) assessment can be provided to the customer on cost

interventions for comfort improvements and energy efficiency which would draw an

additional premium. These interventions can vary from improving the air-

conditioning system efficiency to building envelope thermal property improvements.

A study by Viginie & Michael (2007) shows that by improving the EER of the air-

conditioning system from 2.34 to 2.81 an improvement of 17% in terms of energy

consumption is observed based on LCC analysis. Similarly an LCC based study on

influence of building thermal insulation on cooling load in the hot and humid climates

(Aktacir et al, 2010) shows that with improvement to the wall insulation a saving of

17-20% can be obtained. Studies by Banfi et al (2008) in Swiss residential buildings

showed that the benefits of the energy-saving attributes are significantly valued by the

consumers and strongly influenced their willingness to pay for energy-saving

measures. Investigations by Nair et al (2010) have shown that personal attributes

such as income, education, age and contextual factors, including age of the house,

thermal discomfort, past investment, and perceived energy cost, preference for a

particular type of energy efficiency measure influenced the energy efficiency

investments in existing residential buildings.

Though this is a promising approach towards energy efficiency, the inherent inter-

residence dissimilarity in comfort and energy use in this building typology described

in section 6.3 and 6.4 poses the challenge of setting benchmarks. This is primarily due

to the fact that part of the residential units will require high cost interventions, while

part of them might naturally meet the benchmarks. In this case it would be required

to quantify the economic interventions for individual residential units. Since these

condominiums are multi-tenant occupancy based models, the commercial

implications and saleability issues would be equally challenging. A typical example

in the context of the present study is discussed further.

Figure 18. Cooling energy demand – Block A

Fig. 18 presents the relation between ambient DDH (based on Tout) for conditioned

hours and cooling energy demand for residential units 1 and 4 in condominium block

A. The average cooling energy demand for block A is shown through the solid trend

line, while the dotted trend lines represent those of unit 1 and 4. For the purpose of

setting an upper limit for cooling energy consumption, the Trm for summer months

(March to July) were statistically analysed and the 90 percentile value (32oC) was

obtained. From this, the corresponding Tn was estimated (30.6oC) using eq. 1. The

DDH cut-off for a Tn of 29.1oC was estimated to be 8.9 degree hours. From fig. 17 it

can be found that the cooling energy demand for this DDH cut-off corresponds to

32.5 KWh/day with respect to block A average. Notably, the corresponding cooling

energy demands for unit 4 stands at 35 KWh while that of unit 1 stands at 30.1 KWh.

This amounts to about 14% variation in cooling energy demand between the two

adjacent residential units with a similar design and built-up area.In order to be more

representative, this quantitative difference needs to be considered in the LCC analysis

for comfort and energy efficiency interventions either in terms of HVAC system

efficiency or envelope thermal property improvements.

8 Conclusions

Thermal comfort and related energy consumption scenario in condominium style

residential buildings were presented in the study. Analysis was focused on residential

units distributed among four identical blocks. Subjective surveys administered with

the residents on the air conditioner usage pattern revealed that the set point

temperatures and operation duration were highly non-uniform in nature. Statistical

mean value of 23.5oC in terms of set point temperature and 6.5 hours of air

conditioner usage per day were obtained from the study. Under naturally ventilated

conditions, the indoor diurnal variations ranged between 4 to 5oC as compared to the

Tout variation of about 10oC. Thermal lag between Tout and Tin ranged from 1 to 2.5

hours. Maximum daily heat gains varied from 15 to 20 W/m2 and heat losses varied

from 5 to 8 W/m2. Heat discomfort was prevalent during summer (ePMV +1.5 to

+2.8) and thermal comfort/neutrality was prevalent during winter (ePMV -0.5 to +0.8).

A greater extent of indoor temperatures was found to be outside the acceptability

criteria of adaptive comfort in terms of Trm, during peak summer (May and early

June).

The measured indoor thermal comfort and associated energy consumption were found

to be in good agreement with the simulated results. Thermal discomfort severity was

estimated in terms of night time DDH which was found to exhibit a strong and linear

correlation with the ambient daily Tout maxima irrespective of the design

configurations. Though Tin maxima and minima in various zones of a residential unit

exhibited similarity, the magnitude and frequency of DDH were found to considerably

different from each other. These variations were also noticeable in the cooling energy

variations of various zones. Thermal discomfort was more uniform for residential

units with similar solar exposure conditions. Mutual shading by adjacent blocks was

found to have a significant effect on both the discomfort and cooling energy estimates.

Residential units located at the higher floor levels were found to have marginally

higher cooling energy demand compared to those in the lower floor level. The trend

in simulated and actual energy consumption pattern was comparable only among the

flats exposed to critical orientations. The pattern of consumption was found to be

non-uniform in the other orientations.

Viability of interventions at the design stages and post occupancy stages for

improvement in thermal comfort and reduction in cooling energy were also explored.

A prototype matrix was presented for evaluation of thermal severity considering both

the predicted comfort as well as the usage pattern of the residents. The method of

ranking various zones in a given residential unit as well as a comparative evaluation

of various residences forms a useful tool for both the residents as well as the designers

to take a relook at the commercial value of thermal comfort and cooling energy in this

building type. For the commercial interventions on energy efficiency to be more

representative, the inherent comfort variations among residential units needs to be

considered in the LCC analysis.

9 Scope for further studies

The findings of the study throws light on the inbuilt non-uniformities in thermal

comfort and energy consumption in condominium type residential developments.

Detailed studies involving residential unit level power consumption measurements

and user behavioural pattern is essential for evolving more conclusive outcomes.

Further studies on limitations to adaptation in such residential type needs to be

explored so that a judicious estimate of adaptation can be arrived. Studies relating to

life cycle cost estimates on thermal comfort improvements and its commercial

viability in the market needs to be explored to ensure effective implementation.

Acknowledgements

We wish to acknowledge the inputs form Ms. Anupama Udaykumar and the support

extended by the facility management team and residents of the gated community.

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Appendix – A