impact of clothing and activity level on energy...
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Impact Of Clothing and Activity Level On Energy Consumption and
Thermal Comfort On The Occupants In The Residential Building In
Cold climate Region.
Sadaf Alam1,*
1 Department of Energy Technology, School of Engineering, Aalto University, Finland
ABSTRACT
In this paper, various clothing level and metabolic rates have been simulated and
analyzed to study the impact on the thermal comfort and energy efficiency of the
detached house in cold climate region Finland.
The main objective of this paper is to show how much minimum and maximum
indoor air temperature can be achieved while fulfilling the target values for thermal
conditions selected and defined according to SFS-EN—15251. PMV and PPD have
been calculated in order to show the satisfaction crieterias.
KEYWORDS
Thermal comfort, PMV, PPD
INTRODUCTION
Occupant’s comfort role has risen further in relative terms, as lighting, building
envelopes, and heating, ventilation, and air-conditioning (HVAC) equipment have
improved in efficiency; meanwhile comfort expectations have increased (D. Kaneda et
al. 2010 , P. Hoes et al. 2009)
Employing efficient building techniques can go a long way in keeping the electricity
costs down, long term economic and infrastructure benefits. However, Occupant’s
comfort is major need and concern for evaluating the performance of a building
control system, since a poor indoor comfort can directly effect on productivity and
efficiency as described in (ASHRAE, 2010).
(ASHRAE, 2010) says, the amount of thermal insulation worn by a person has a
substantial impact on thermal comfort (Clothing adjustment is a behaviour that directly
affects the heat-balance. The thermal insulation provided by garments and clothing
ensembles is expressed in a unit named clo, where 1 clo is equal to 0.155 m2 K/W. For
near-sedentary activities where the metabolic rate is approximately 1.2 met, the effect
* Corresponding author email: [email protected]
657
of changing clothing insulation on the optimum operative temperature is approximately
6 °C per clo. Clothing adjustment is perhaps the most important of all the thermal
comfort adjustments available to occupants in office buildings (Newsham GR. 1997)
As mentioned by (Fanger .1997) , Clothing is one among the six variables (others are:
Air temperature, mean radiant temperature, air speed, relative humidity and metabolic
activity) that affects the calculation of the predicted mean vote (PMV) and predicted
percentage of dissatisfied (PPD) and therefore is an input for thermal comfort
calculations according to (ASHRAE .2010 ) , European (ISO. ISO 7730. 2005) and
International thermal comfort standards. The selection of the clothing insulation for
thermal comfort calculations affects the design (sizing and analysis) of HVAC systems,
the energy evaluation and the operation of buildings. Previous attempts to develop a
dynamic clothing model demonstrated that the ability to more accurately predict
variations in clothing leads to improved thermal comfort (Newsham GR.. 1997),
smaller HVAC size and lower energy consumption (De Carli M et al. 2007). (De Carli
M et al. 2007), concluded that in mechanically conditioned buildings a variation of
0.1 clo is sufficient to significantly affect the comfort evaluation based on the
PMV–PPD model.
Although there are studies (De Carli M, et al 2007, De Dear R et al. 1997, Faraway JJ.
2006 and Morgan C, et al. 2003) that has addressed clothing insulation in mechanically
ventilated buildings, however the thermal comfort of the occupants has not been
adequately addressed and discussed. We attempt to fill this gap by addressing the
acceptable range of the operative temperature and indoor air temperature to maintain
the thermal comfort of the occupants.
RESEARCH METHODOLOGY AND ASSUMPTIONS
Fanger approach
In our present study we have used ( Fanger 1970 ) approach to predict the thermal
performance of the building. Study compares the (Predicted Mean Vote (PMV) index
on 7 point thermal sensation scale, which is being calculated using the effective
operative (ISO7730:2005) and air temperatures. Thermal comfort has a great
influence on the productivity and satisfaction of indoor building occupants (Roberto
et al. 2007).
Following Table 1 shows, the thermal sensation scale of the thermal comfort
following Fanger, quantified by an adapted ASHRAE 7 – point scale
Table 1: The 7-Point Thermal Sensation Scale (Fanger .1970)
PMV Scale Description
-3 Cold
-2 Cool
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-1 Slightly cool
0 Neutral
1 Slightly warm
2 Warm
3 Hot
(Fanger .1970) realized that the vote predicted was only the mean value to be
expected from a group of people, and he extended the PMV to predict the proportion
of any population who will be dissatisfied with the environment.
The Predicted percentage dissatisfied (PPD)-index those who vote outside the range
of -1 (slightly cool to + 1 (slightly warm) scaling points were termed as dissatisfied.
The distribution of PPD is based on observations from climate chamber experiments
and not from field measurements.
Fanger related the PPD index to the PMV index as follows
��� = 100 − 95�( . ��������� .��������)
(1)
This relation has been represented in the figure 1
Figure1. Predicted Percentage of Dissatisfaction (PPD) as a function of Predicted
Mean Vote (PMV) (Fanger,1970)
Figure 1 it can be seen that when PMV index is in between -0.5 and + 0.5, it means
less than 10% will find the thermal environment unacceptable. It can be further seen
from this curve that the minimum PPD is 5 % when PMV value is 0.
Standards relating to this study
In this study we have used two main reference standards SFS- EN 15251 and
ASHRAE- 55 0 0
0
20
40
60
80
-4 -2 0 2 4
PP
D %
PMV
Predicted percentage of dissatisfied (PPD) as a function
of predicted mean
vote (PMV)
PPD %
659
SFS-EN 15251
This European standard (SFS- EN 15251) specifies the indoor environmental
parameters which have an impact on energy performance of the buildings. Table 2 and
table 3, show the thermal comfort requirements for different categories of
environment and types of space.
Table 2: Description of the applicability of the categories used (SFS EN ISO 15251,
2007)
Category Explanation
I High level of expectation and is recommended for spaces occupied by very
sensitive and fragile persons with special requirements like
handicapped, sick, very young children and elderly persons
II Normal level of expectation and should be used for new buildings and
renovations
III An acceptable, moderate level of expectation and may be used for existing
buildings
IV Values outside the criteria for the above categories. This category should
only be accepted for a limited part of the year
Table 3: Comfort categories valid for mechanically heated and cooled buildings (SFS
EN ISO 15251, 2007)
Category Thermal state of the body as a whole
Predicted percentage of dissatisfied PPD % Predicted Mean Vote
I < 6 -0.2 < PMV < +0.2
II < 10 -0.5 < PMV < +0.5
III < 15 -0.7 < PMV < +0.7
I V > 15 PMV < - 0.7 or +0.7 < PMV
ASHRAE- 55
In our study we have used table 4 ) ASHRAE s-55 , 2010) to define the maximum
change in Operative temperature allowed during a period of time.
Table 4: Limits on Temperature Drifts and Ramps
Time Period, h 0.25 0.5 1 2 4
Maximum Operative
Temperature , °C 1.1 1.7 2.2 2.8 3.3
660
This table 4 explains that the , operative temperature (Top) may not change more than
2.2°C during a 1.0 h period, and it also may not change more than 1.1°C during any
0.25 h period within that 1.0 h period. If variations are created as a result of control or
adjustments by the user, higher values may be acceptable.
Building Simulation tool IDA ICE
In this paper for our study we have used IDA ICE building simulation software
(Sahlin P ,1996 and Björsell N). IDA Indoor Climate and Energy (IDA ICE) is a
whole year detailed and dynamic multi-zone simulation application for the study of
indoor climate as well as energy. Different case studies have been validated IDA ICE
(Moinard S et al. 1999)
Weather Data and Location
In our study, Finnish test reference year (TRY2012) is being used as a weather data
for energy simulations. Finland belongs wholly to the temperate coniferous-mixed
forest zone with cold, wet winters. The annual average temperature for Helsinki –
Vantaa region is + 5.4°C (Targo Kalameesa, et al. 2012) and it comes under climate
zone 1. The average number of degree days (at indoor temperature 17) is 3952.
Building description
In this paper we have simulated Finnish single family detached house having heated
area of 180 m2
and air volume of 468 m3 The building has major four occupied zones,
U-values and air tightness of the 1960 Light weight case based on the default values of
the statement of Finnish energy certificate 2013.
We have taken the activity levels (according to SFS EN ISO 15251) of 1 met (Seated
and relaxed) and 1. 2 met (Standing and relaxed) and clothing insulation levels of 0.96
clo (Trousers, long sleeved shirts plus suit jackets) and 1.14 clo (Trousers, long
sleeved shirts plus suit jackets plus vest and T- shirt).
RESULTS AND DISCUSSION
In this study we have done hourly dynamic simulation on IDA ICE and analyzed
building cases with different clothing and activity level. Fanger approach has been
applied to predict the thermal performance of the buildings. An example has been
showed in figure2.
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Figure 2. Minimum Top and Minimum Tair at different levels of thermal insulation
Above figure 2 shows the minimum operative temperature (top) and air tem
(tair) at different levels of thermal insulation fulfilling the comfort conditions for air
conditioned buildings mentioned in SFS
no thermal comfort violations, and then we have calculated the minimum o
temperatures at different level of thermal comfort which has been showed and
discussed in the below tables
level and clothing level.
Table 5: Min Top and min Tair of 1960 LW at different activity and clothing level
Category
PPD
PMV
1960 L
Min
Top
I
II
III
< 6
< 10
< 15
-0.2<PMV<+0.2
-0.5<PMV<+0.5
-0.7<PMV<+0.7
23.5
22.4
19.6
To implement this, the coldest period (8th of January
determine critical values of PMVs (minimum and maximum) and indoor temperatures.
As the results of this strategy, PMVs and indoor temperature are drawn to find linear
trendline of PMV vs indoor temperature.
increased amount of activity and clothing levels can help in decreasing the operative
temperature in certain amount approximately by 0.5
-0.9-0.8-0.7-0.6-0.5-0.4-0.3-0.2-0.1
00.10.20.30.40.50.60.70.80.9
20 21
PM
V
PMV value
Linear line of maximum PMV
Minimum Top and Minimum Tair at different levels of thermal insulation
shows the minimum operative temperature (top) and air tem
(tair) at different levels of thermal insulation fulfilling the comfort conditions for air
ed buildings mentioned in SFS-EN 15251 standard. We can see that there are
no thermal comfort violations, and then we have calculated the minimum o
temperatures at different level of thermal comfort which has been showed and
discussed in the below tables 5 and 6 for different building cases at different
Min Top and min Tair of 1960 LW at different activity and clothing level
1960 LW 1 met 1960LW 1.2 met
0.96 clo 1.14 clo 0.96 clo
Min
Top ◦C
Min
Tair
◦C
Min
Top
◦C
Min
Tair
◦C
Min
Top ◦C
Min
Tair
◦C
23.5
22.4
.6
24.2
23.1
22.3
22.9
21.6
20.7
23.4
22
21.1
22
20.5
19.5
22.3
20.8
19.8
To implement this, the coldest period (8th of January-4th of March) was simulated to
determine critical values of PMVs (minimum and maximum) and indoor temperatures.
As the results of this strategy, PMVs and indoor temperature are drawn to find linear
trendline of PMV vs indoor temperature. From table 5, results shows that
increased amount of activity and clothing levels can help in decreasing the operative
temperature in certain amount approximately by 0.5 ◦C.
PMV = 0.205AT - 4.71
22 23 24 25 26
Air temperature
Linear line of minimum PMV
Linear line of maximum PMV Linear (PMV value)
Minimum Top and Minimum Tair at different levels of thermal insulation
shows the minimum operative temperature (top) and air temperature
(tair) at different levels of thermal insulation fulfilling the comfort conditions for air
can see that there are
no thermal comfort violations, and then we have calculated the minimum operative
temperatures at different level of thermal comfort which has been showed and
for different building cases at different activity
Min Top and min Tair of 1960 LW at different activity and clothing level
1960LW 1.2 met
1.14 clo
Min
Top ◦C
Min
Tair ◦C
20.8
19.2
18.2
21.1
19.5
18.4
ch) was simulated to
determine critical values of PMVs (minimum and maximum) and indoor temperatures.
As the results of this strategy, PMVs and indoor temperature are drawn to find linear
results shows that, by
increased amount of activity and clothing levels can help in decreasing the operative
26
662
Table 6: Comparison of Min and Max Operative and Air temperatures for 1960LW
(1.2 met, 0.96 clo) with electrical radiators with air velocity= 0.1 m/sec
Category
PPD
PMV
1960 LW_1.2 met, 0.96 clo 1960 LW_1.2 met, 0.96 clo
Electric radiators , V= 0.1m/sec Electric radiators , V= 0.1 m/sec
Min Top ◦C Min Tair
◦C
Max Top ◦C
Max Tair ◦C
I
II
III
< 6
< 10
< 15
-0.2<PMV<+0.2
-0.5<PMV<+0.5
-0.7<PMV<+0.7
22
20.5
19.5
22.3
20.8
19.8
23
24.4
25.3
23.3
24.7
25.6
From the table 6, it can be seen that the maximum operative temperature can be 25 ◦C
and minimum can be 19.6 ◦C approximately in 1960 light weight buildings having
electric heating systems.
CONCLUSION
This study presented acceptable range of indoor air and operative temperatures of a
detached house in a cold climate of Finland fulfilling thermal comfort categories
recommended by SFS-EN 15251 standard. The amount of temperature decrease
depends on activity and clothing levels of occupants, air velocity, building properties
such as heat distribution system, thermal insulation, and thermal mass.
ACKNOWLEDGEMENTS
I would like to dedicate my acknowledgment of gratitude towards my instructor and
professor who gave me an opportunity to work on SAGA project.
REFERENCES
D. Kaneda, B. Jacobson, P. Rumsey, R. Engineers, Plug load reduction: the next big
hurdle for net zero energy building design. ACEEE summer study on energy
efficiency in buildings, Pacific Grove, CA (2010), pp. 120–130
P. Hoes, J.L.M. Hensen, M.G.L.C. Loomans, B. de Vries, D. Bourgeois: User behavior
in whole building simulation, Energy Build, 41 (2009), pp. 295–302
ASHRAE, ASHRAE standard 55, thermal environmental conditions for human
occupancy. American Society of Heating Refrigeration and Air conditioning
Engineers; 2010
Newsham GR. Clothing as a thermal comfort moderator and the effect on energy
consumption. Energy Build 1997; 26:283- 91.
Fanger PO. Thermal comfort. Copenhagen: Danish Technical Press; 1970. CEN. EN
15251- 2007. Criteria for the indoor environment including thermal, indoor air
quality, light and noise. European Committee for Standardization; 2007.
ISO. ISO 7730. Moderate thermal environment e determination of the PMV and PPD
663
indices and specification of the conditions for thermal comfort. International
Organization for Standardization; 2005.
Hensen JLM, Lamberts R. Building performance simulation for design and operation.
Taylor and Francis; 2011.
De Carli M, Olesen BW, Zarrella A, Zecchin R. People’s clothing behaviour according
to external weather and indoor environment. Build Environ 2007; 42:3965-73.
De Dear R, Brager G. Developing an adaptive model of thermal comfort and
preference. Final report ASHRAE RP-884, 1997.
Faraway JJ. Extending the linear model with R: generalized linear, mixed effects and
nonparametric regression models. CRC Press; 2006.
Morgan C, De Dear R. Weather, clothing and thermal adaptation to indoor climate.
Clim Res 2003; 24:267- 84.
ASHRAE, ASHRAE standard 55, thermal environmental conditions for human
occupancy. American Society of Heating Refrigeration and Air conditioning
Engineers; 2010
International Organization for Standardization, ISO7730:2005, Ergonomics of the
Thermal Environment-Analytical Determination and Interpretation of Thermal
Comfort Using Calculation of PMV and PPD Indices and Local Thermal Comfort
Criteria, 2005.
Fanger P. Thermal comfort: analysis and applications in environmental Engineering.
United States: McGraw-Hill Book Company; 1970.
Roberto, Z.F., G.H.C. Oliveira and N. Mendes, 2008. Predictive controllers for
thermal comfort optimization and energy savings. Energy. Buildings. 40:
1353-1365. DOI: 10.1016/j.enbuild.2007.12.007
European Standardization Organization. 2007. SFS EN ISO 15251, indoor
environmental input parameters for design and assessment of energy performance
of buildings addressing indoor air quality, thermal environment, lighting and
acoustics.
Sahlin P. Modelling and simulation methods for modular continuous system in
buildings, PhD Thesis, KTH, Stockholm, Sweden; 1996.
Björsell N, Bring A, Eriksson L, Grozman P, Lindgren M, Sahlin P, et al. IDA indoor
climate and energy. In: Proceedings of the IBPSA building simulation ’99
conference, Kyoto, Japan; 199
Moinard S, Guyon G. editors. Empirical validation of EDF ETNA and GENEC
test-cell models, Subtask A.3, A Report of IEA Task 22, Building Energy Analysis
Tools; 1999.
Travesi J, Maxwell G, Klaassen C, Holtz M. Empirical validation of Iowa energy
resource station building energy analysis simulation models, IEA Task 22,
Subtask A; 2001
Targo Kalameesa, et al. Development of weighting factors for climate variables for
selecting the energy reference year according to the EN ISO 15927-4 standard,
Energy and Buildings 47 (2012) 53–60.
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