Aug 17, 2016 - range of comfort temperatures and other requirements to maintain comfortable indoor ... occupants in hot and humid conditions in Malaysia, Singapore, Indonesia, and .... Nationality distribution in each investigated country. 77.
THERMAL COMFORT FIELD STUDY IN OFFICE BUILDINGS WITH DIFFERENT VENTILATION MODES IN HOT AND HUMID CONDITIONS
SITI AISYAH DAMIATI
UNIVERSITI TEKNOLOGI MALAYSIA
PSZ 19:16 (Pind. 1/07)
UNIVERSITI TEKNOLOGI MALAYSIA DECLARATION OF THESIS / UNDERGRADUATE PROJECT REPORT AND COPYRIGHT Author’s full name : SITI AISYAH DAMIATI Date of Birth
: 17 DECEMBER 1990
Title
: THERMAL COMFORT FIELD STUDY IN OFFICE BUILDINGS WITH DIFFERENT VENTILATION MODES IN HOT AND HUMID CONDITIONS
Academic Session : 2016/2017 I declare that this thesis is classified as:
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thesis for academic exchange. Certified by:
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Date:
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: Dr. Sheikh Ahmad Zaki Shaikh Salim
Date
27 January 2017 : ______________________________
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THERMAL COMFORT FIELD STUDY IN OFFICE BUILDINGS WITH DIFFERENT VENTILATION MODES IN HOT AND HUMID CONDITIONS
SITI AISYAH DAMIATI
A thesis submitted in fulfilment of the requirements for the award of the degree of Master of Philosophy
Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia
JANUARY 2017
ii DECLARATION
I declare that this thesis entitled “Thermal Comfort Field Study in Office Buildings with Different Ventilation Modes in Hot and Humid Conditions” is the result of my own research except as cited in the references. The thesis has not been accepted for any degree and is not concurrently submitted in candidature of any other degree.
Signature
:
________________
Name
:
Siti Aisyah Damiati
Date
:
27 January 2017 ________________
iii DEDICATION
I dedicate this thesis for knowledge, which is three hand-spans: the first breeds arrogance, the second breeds humility, and in the third, you realize you know nothing.
iv
ACKNOWLEDGEMENT
Praise be to Allah for granting me strength, faith, and capability to finish this study. The path toward this thesis had been circuitous. Its completion was thanks in large part to the special people who challenged, supported, and stuck with me along the way. My utmost appreciation to my supervisor, Dr. Sheikh Ahmad Zaki bin Shaikh Salim, for his continuous guidance, support, and hardwork. I would also like to express my gratitude to Dr. Hom Bahadur Rijal from Tokyo City University (TCU), for his profound knowledge sharing and guidance in this research. I am hugely indebted to Dr. Surjamanto Wonorahardjo from Institut Teknologi Bandung (ITB), for helping me through crucial first steps into the world of academia. During data collection phase, I received help from many people in various institutions. My sincere thanks to: Dr Azli Abd Razak from Universiti Teknologi MARA Shah Alam campus, his students Aftha and Adam; Prof. Wong Nyuk Hien from National University of Singapore and his student; my surveyor friends in ITB, Rizki Fitria Madina and Nurfadhilah Aslim; Divisi Logistik and SAPPK staffs in ITB; Management of CIMB Niaga Bandung; the students and officers in TCU Yokohama and Setagaya campus; fellow postgraduate students and RA in iKohza CAIRO, IDS, and VSE; and all respondents who participated in this study. My sincere gratitude to Japan-ASEAN Integration Fund (JAIF) for funding me in the course of my study. Needless to say, the completion of my study would be far-fetched without the useful advises and assistance from academicians and friends in MalaysiaJapan International Institute of Technology (MJIIT), particularly my colleagues in iKohza WEE. Special mention to A. Syahid, for morale and technical support, and always being there for me through thick and thin. Last but not least, for unlimited prayers and support from my beloved parents: Yul Wachjudi, Dinny Mardiana, and brothers: M. Iqbal Askia and M. Haunan Nail, I could never thank them enough. Lastly, I apologize to all supportive parties who could not be listed in this limited space.
v
ABSTRACT
Air Conditioning (AC) is a common feature in office buildings to provide comfortable indoor thermal condition which is also the leading cause of the increase in energy consumption. Many research has been carried out to reduce energy consumption without sacrificing the comfort of office occupants by understanding the range of comfort temperatures and other requirements to maintain comfortable indoor thermal condition. Therefore, this study investigates the thermal comfort of office occupants in hot and humid conditions in Malaysia, Singapore, Indonesia, and during summer in Japan. A survey was done and 2,049 responses were obtained from eleven office buildings in these countries, using AC for Cooling purpose (CL), Mixed-Mode (MM) which is a combination of AC and windows, and Free-Running (FR) which uses natural ventilation only. Comfort temperatures were analysed using Griffiths’, regression and probit methods, which were then compared with thermal comfort standards such as ASHRAE Standard 55, CEN Standard EN15251 and the CIBSE guide. Analysis using the Griffiths’ method showed that the occupants mean comfort operative temperatures in CL mode were 26.4 ºC, 26.3 ºC, and 25.6 ºC in Singapore, Indonesia and Malaysia, respectively. Meanwhile in Indonesia, the comfort operative temperatures were 27.5 ºC in MM and 24.7 ºC in FR. However in Japan, the comfort temperature was 25.8 ºC for both CL and FR modes. Due to insufficient data range, the results from regression and probit methods were insignificant in some locations and was omitted. However, most of the results were compatible with international standards, while the comfort temperature range of occupants in Singapore, Indonesia, and Malaysia were higher than local regulation in each country. Additionally, in terms of adaptive behaviour, it was found that most of the occupants in Malaysia more frequently used AC systems to maintain thermal comfort compared to those in Indonesia and Japan. Conclusively, the results from this study could be a useful reference for architects and practitioners in design stages for smart buildings, as well as an update to the local regulations concerning indoor thermal conditions in office buildings.
vi
ABSTRAK
Penyaman Udara (AC) merupakan alat yang biasa digunakan di dalam bangunan pejabat untuk memberikan keselesaan terma, tetapi merupakan penyebab utama penggunaan tenaga secara berlebihan. Banyak penyelidikan telah dijalankan untuk mengurangkan penggunaan tenaga tanpa mengorbankan keselesaan pekerja pejabat dengan memahami julat suhu yang selesa dan keperluan lain untuk mengekalkan keadaan keselesaan terma dalaman. Oleh itu, kajian ini menyelidik keselesaan terma pekerja pejabat dalam keadaan cuaca yang panas dan lembap di Malaysia, Singapura, Indonesia, dan pada musim panas di Jepun. Satu kajian telah dilakukan dan 2,049 maklumbalas diperoleh daripada 11 bangunan pejabat di negaranegara tersebut, yang menggunakan mod pengudaraan Penyaman Udara (CL), Mod Campuran (MM), yang merupakan kombinasi AC dan tingkap, dan Pengudaraan Semulajadi (FR). Suhu keselesaan dianalisis menggunakan kaedah Griffiths', regresi, dan probit, yang kemudiannya dibandingkan dengan piawaian keselesaan terma seperti ASHRAE Standard 55, CEN Standard EN15251 dan garispanduan dari CIBSE. Analisis menggunakan kaedah Griffiths' menunjukkan bahawa suhu keselesaan dalam mod pengudaraan CL adalah masing-masing 26.4 ºC, 26.3 ºC, dan 25.6 ºC di Singapura, Indonesia dan Malaysia. Sementara itu di Indonesia, suhu keselesaan adalah 27.5 °C bagi MM dan 24.7 ºC bagi FR. Namun di Jepun, suhu keselesaan adalah 25.8 ºC untuk kedua-dua mod CL dan FR. Oleh kerana julat data yang tidak mencukupi, hasil daripada kaedah regresi dan probit adalah kecil pada sesetengah lokasi dan telah diabaikan. Walau bagaimanapun, kebanyakan hasil adalah setara dengan piawaian antarabangsa, tetapi julat suhu keselesaan bagi penghuni di Singapura, Indonesia, dan Malaysia adalah lebih tinggi daripada piawaian tempatan di setiap negara tersebut. Selain itu, didapati bahawa kebanyakan penghuni di Malaysia lebih kerap menggunakan AC untuk mengekalkan keselesaan terma berbanding dengan penghuni di Indonesia dan Jepun. Pada kesimpulannya, hasil daripada kajian ini boleh menjadi rujukan yang berguna untuk arkitek dan pengamal bangunan pintar pada tahap rekabentuk, serta dapat mengemaskini piawaian tempatan keadaan terma dalaman di bangunan pejabat.
vii
TABLE OF CONTENTS
CHAPTER
TITLE DECLARATION
ii II
DEDICATION
iiiIII
ACKNOWLEDGEMENT
ivIV
ABSTRACT
vV
ABSTRAK
viVI
TABLE OF CONTENTS
VII vii
LIST OF TABLES
xiXI
LIST OF FIGURES
XIV xiv
LIST OF SYMBOLS
XIX xix
LIST OF ABBREVIATIONS
XXI xxi
LIST OF APPENDICES 1
2
PAGE
XXII xxii
INTRODUCTION
1
1.1
Introduction
1
1.2
Research Background
1
1.3
Problem Statement
3
1.4
Research Objectives
4
1.5
Research Questions
5
1.6
Research Scope and Limitation
5
1.7
Research Significance
6
1.8
Thesis Structure
6
1.9
Chapter Summary
7
LITERATURE REVIEW
9
2.1
Introduction
9
2.2
Basic Theory
9
viii 2.2.1 Heat balance equation
10
2.2.2 Predicted Mean Vote (PMV) and Predicted Precentage of Dissatisfied (PPD) 2.2.3 Adaptive Thermal Comfort
15
Factors Affecting Thermal Comfort
19
2.3.1 Indoor air and radiant temperatures
19
2.3.2 Air velocity
20
2.3.3 Humidity
21
2.3.4 Clothing Insulation
22
2.3.5 Metabolic rates
24
2.3.6 Demographics
25
Field Studies in Office Buildings
28
2.4.1 Field studies in tropical climate
28
2.4.2 Field studies in Japan
30
2.5
Building Ventilation Modes
33
2.6
Related Standards
36
2.7
Chapter Summary
39
2.3
2.4
3
13
METHODOLOGY
40
3.1
Introduction
40
3.2
Overall Structure of Methodology
40
3.3
Climate and Geographical Description
41
3.4
Investigated Office Buildings
45
3.4.1 Singapore
47
3.4.2 Indonesia
50
3.4.3 Malaysia
53
3.4.4 Japan
59
Data Collection Method
61
3.5.1 Field Measurement
61
3.5.2 Questionnaire Survey
64
Estimation of Thermal Comfort Variables
66
3.6.1 Thermal indices
67
3.6.2 Absolute Humidity
68
3.5
3.6
ix 3.6.3 Body Surface Area
69
Analytical Techniques
70
3.7.1 Regression method
70
3.7.2 Probit analysis method
71
3.7.3 Griffiths’ method
71
Chapter Summary
72
EVALUATION OF RESULTS
73
4.1
Introduction
73
4.2
Descriptive Measures
73
4.2.1 Demographic distribution
74
4.2.2 Nationality distribution
77
4.2.3 Clothing Insulation and Metabolic rates
78
Climatic Measurement Results
82
4.3.1 Outdoor air temperature
84
4.3.2 Indoor temperature
87
4.3.3 Indoor humidity
91
4.3.4 Indoor air velocity
94
Questionnaire Survey Results
96
4.4.1 Health condition and overall comfort
98
3.7
3.8 4
4.3
4.4
5
4.4.2 Thermal sensation vote
100
4.4.3 Thermal preference and acceptance
103
4.4.4 Humidity feeling and preference
106
4.4.5 Air movement vote and acceptance
108
4.5
Predicted Mean Vote and Percentage of Dissatisfied
110
4.6
Chapter Summary
114
ANALYSIS AND DISCUSSION
115
5.1
Introduction
115
5.1
Comfort Temperature
115
5.1.1 Regression analysis
116
5.1.2 Probit analysis
118
5.1.3 Griffiths’ method
121
x 5.1.4 Comparison of all methods and predicted results
125
5.1.5 Compliance with standards and guidelines
128
5.1.6 Comparison with previous studies
131
Factors Affecting Thermal Comfort
134
5.2.1 Correlations of thermal comfort parameters
134
5.2.2 Thermal comfort and humidity
136
5.2.3 Thermal comfort and air velocity
140
5.2.4 Thermal comfort and demographics
144
5.3
Adaptive Behaviour
148
5.4
Chapter Summary
153
5.2
6
CONCLUSIONS
154
6.1
Introduction
154
6.2
Concluding Remarks
154
6.2.1 Comfort temperature
155
6.2.2 Factors affecting thermal comfort
156
6.2.3 Adaptive behaviour
157
Study Implications
157
6.3.1 Theoretical implications
158
6.3.2 Practical implications
158
Study Limitations and Future Research
159
6.4.1 Study limitations
160
6.4.2 Recommendation for future studies
161
6.3
6.4
REFERENCES Appendices A - D
162 176 - 192
xi
LIST OF TABLES
TABLE NO.
TITLE
PAGE
2.1
Sample of clothing insulation ensemble (ASHRAE, 2013)
23
2.2
Metabolic rates for common activities (ASHRAE, 2013)
25
2.3
List of definitions related to ventilation
33
3.1
Investigated offices in each country
46
3.2
Detail information for each investigated offices in Singapore
47
3.3
Detail information for each investigated offices in Indonesia
51
3.4
Detail information for each investigated working spaces in Kuala Lumpur, Malaysia
54
Detail information for investigated offices in UiTM Shah Alam, Malaysia
58
3.6
Detail information for investigated offices in Japan
60
3.7
Instrument Specifications for Field Measurement
62
3.8
Scale for thermal comfort questionnaire
66
4.1
Distribution of age, weight, height, and body surface area
75
4.2
Nationality distribution in each investigated country
77
4.3
Distribution of occupants' clothing insulation values
80
4.4
Average values for climatic parameters in each country with different ventilation modes
83
3.5
4.5
Distribution of outdoor temperature during survey and comparison with running mean, daily mean, and monthly mean outdoor air temperature 85
4.6
Outdoor temperature variations in specific weather
87
xii 4.7
Regression equations and correlation coefficients of air temperature and other thermal indices
90
4.8
Descriptive statistics of relative humidity
91
4.9
Descriptive statistics of absolute humidity
93
4.10
Air velocity in each case study
95
4.11
Average values for personal parameters in each country and ventilation mode
96
4.12
Percentage of health condition in each investigated location
98
4.13
Percentage of overall comfort votes in each investigated location
99
4.14
Percentage of each thermal sensation vote in each investigated location 101
4.15
Percentage of each thermal preference in each investigated location
103
Percentage of each thermal acceptance in each investigated location
104
Percentage of each humidity feeling in each investigated location
106
Percentage of each humidity preference in each investigated location
107
Percentage of each air movement vote in each investigated location
109
Percentage of air movement acceptance in each investigated location
109
Linear regression analysis of TSV with indoor operative temperature
117
Probit analysis of TSV and indoor operative temperature as covariate
119
Comfort operative temperatures using various Griffiths constants
122
Griffiths’ comfort temperature and mean temperatures for given votes
124
Linear regression analysis of PMV with indoor operative temperature
126
4.16 4.18 4.19 4.20 4.21 5.1 5.2 5.3 5.4 5.5
xiii 5.6
Comparison of results from different methods
128
5.7
Comparison of comfort temperatures of the current study with previous studies 132
5.8
Bivariate correlations of TSV and objective parameters in each ventilation mode 135
5.9
Correlation coefficient of humidity subjective votes and comfort temperature of occupants in Japan
138
5.10
Mean comfort temperature for each humidity preference vote 139
5.11
Linear regression equations of comfort operative temperature with humidity feeling and humidity preference in Japan 140
5.12
Correlation of comfort temperature and thermal sensation vote with air velocity, air movement vote and acceptance 141
5.13
Linear regression equations of comfort operative temperature with air movement vote 142
5.14
Mean comfort temperature for each air movement vote
5.15
Regression equation for comfort temperature between genders in each mode 144
5.16
Correlation coefficient of body surface area against comfort temperature and thermal sensation vote
142
146
xiv
LIST OF FIGURES
FIGURE NO. 2.1
TITLE
PAGE
The predicted percentage of dissatisfied (PPD) as a function of the predicted mean vote (PMV) index
15
Comfort operative temperature in different air speed level (ASHRAE, 2013)
21
2.3
Thailand ventilation chart (Khedari et al., 2000)
30
2.4
Poster of setsuden movement (Ocheltree, 2011)
31
2.5
Clothing adaptation during Cool biz campaign in Japan (Indraganti et al., 2013): a) using lightweight shirt and hand fan, b) clothing and hair style adjustments
31
Mechanical cooling (CL) ventilation mode (Center for the Built Environment (CBE) - University of California Berkeley, 2013)
34
Concurrent mixed-mode operation (Center for the Built Environment (CBE) - University of California Berkeley, 2013)
35
Acceptable Top ranges for naturally conditioned spaces (ASHRAE, 2013)
37
Comfortable indoor Top for buildings without mechanical cooling systems, as a function of the outdoor running mean temperature (CEN, 2007)
38
3.1
Overall structure of methodology
41
3.2
Map of investigated locations, each country is indicated by a red circle (Cartographic Research Laboratory - The University of Alabama, 2016)
42
Annual variation of monthly statistics of outdoor air temperature of year 2015 and measurement timeline for each country
44
2.2
2.6
2.7
2.8 2.9
3.3
xv 3.4
Investigated building's facade in Singapore a) S1, b) S2
48
3.5
Seating and instruments layout in Singapore a) Office S1, b) Office S2
49
3.6
Working area in a) Office S1, b) Office S2
50
3.7
Investigated buildings facade in Indonesia a) Office I1, b) Office I2, c) Office I3
52
Seating and instruments layout in Office I3 (Numerals refer to occupants’ seating position number)
53
3.9
Investigated building’s façade in Kuala Lumpur, Malaysia
54
3.10
Seating and instruments layout in Lab 1 a) Room A and B, b) Room C
55
Photo of working area in Lab 1 a) Room A, b) Room B, c) Room C
56
3.12
Seating and instruments layout in a) Lab 2, b) Lab 3
56
3.13
Photo of postgraduate labs a) Working area in Lab 2, b) Unoccupied space in Lab 2 nearby windows, c) Working space in Lab 3
57
Photo of working area in Lab 4 a) south and east walls, b) north area of work place
57
3.15
Seating and instruments layout in Lab 4
58
3.16
Photo of working area in UiTM Shah Alam, Malaysia a) Office M2-a, b) Office M2-b
59
Investigated buildings’ facade in Japan a) Office J1, b) Office J2
59
3.18
Photo of working area in Japan a) Office J1, b) Office J2
61
3.19
Instruments for indoor field measurements a) Thermo recorder with external sensor and black 40mm sphere, b) Hot-wire anemometer with probe sensor, c) Infrared thermometer for surface temperature
62
The instruments set up on a retort stand with adjustable height
63
3.21
Graphtec midi logger type GL820
64
3.22
Instrument for recording outdoor temperature, covered with solar radiation shield
64
3.8
3.11
3.14
3.17
3.20
xvi 4.1
Gender distribution by country (n refers to number of sample)
74
Distribution of age by gender of respondents in each country
76
Mean values of respondents’ demographic information with 95% confidence interval a) Weight, b) Height
76
4.4
Distribution of respondents' nationality
78
4.5
Typical clothings worn by respondents a) Male in Japan, b) Female in Japan, c) Male in Malaysia, d) Female in Malaysia
79
Distribution of clothing insulation value by occupants' gender in each country
81
4.7
Metabolic rate of Japanese respondents
81
4.8
Mean outdoor temperature, daily mean outdoor temperature, monthly mean outdoor temperature, and running mean temperature in each country
86
Outdoor temperature frequency proportions during survey in each country
86
Histogram of indoor air temperature in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
88
Scatterplot of indoor air temperatures against outdoor temperatures for each measurement session in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
89
Mean temperature in four thermal indices with 95% confidence interval a) Singapore, b) Indonesia, c) Malaysia, d) Japan
90
Scatter diagram of indoor operative temperature and indoor air temperature
91
Histogram of relative humidity in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
92
Histogram of absolute humidity in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
93
4.16
Scatterplot of absolute humidity and relative humidity
94
4.17
Histogram of air velocity in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
95
4.2 4.3
4.6
4.9 4.10 4.11
4.12
4.13 4.14 4.15
xvii 4.18
Overall comfort votes by gender in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
100
Thermal sensation vote proportions for each measurement days a) CL in Singapore, b) FR in Indonesia, c) MM in Indonesia, d) CL in Indonesia, e) CL in Malaysia, f) FR in Japan, g) CL in Japan
102
Histogram of thermal preference and acceptability in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
105
4.21
Relation of TSV and TP scale for all modes
106
4.22
Histogram of humidity feeling and preference in a) Indonesia, b) Malaysia, c) Japan
108
4.23
Relation of humidity feeling and humidity preference
108
4.24
Histogram of air movement vote and acceptance in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
110
Comparison of mean thermal sensation vote in Singapore a) Prediction, b) Survey
111
Comparison of mean thermal sensation vote in Indonesia a) Prediction, b) Survey
112
Comparison of mean thermal sensation vote in Malaysia a) Prediction, b) Survey
113
Comparison of mean thermal sensation vote in Japan a) Prediction, b) Survey
114
Scatter diagram for regression analysis of comfort temperatures with statistically significant results in a) Indonesia, b) Malaysia
118
Proportion of TSV in study cases with significant probit results a) CL in Malaysia, b) MM in Indonesia, c) CL in Japan, d) proportion of comfortable vote in the three cases
120
Mean comfort temperature with 95% confidence interval in each location based on Griffiths' method a) Singapore, b) Indonesia, c) Malaysia, d) Japan
123
Scatter diagram of PMV against indoor operative temperature in a) Singapore, b) Indonesia, c) Malaysia, and d) Japan
127
4.19
4.20
4.25 4.26 4.27 4.28 5.1
5.2
5.3
5.4
5.5
Comparison of results from each ventilation mode with relevant standard, guideline, and adaptive model a) FR with the ISO Standard EN 15251, b) FR with ASHRAE
xviii Standard 55, c) MM with previous studies in Pakistan and India, d) CL with CIBSE guide
129
Scatterplots of comfort temperature against absolute humidity by ventilation modes in Indonesia and Japan
137
Mean comfort temperatures with 95% confidence interval error bars on humidity preference of occupants in Japan a) FR mode, b) CL mode
138
Mean comfort temperature and mean air velocity on air movement vote
143
Mean comfort temperature with 95% confidence interval error bars for each gender based on Griffiths' method
145
Mean comfort temperatures with 95% confidence interval error bars for each body surface area group
147
5.11
Thermal preference by two groups of body surface area
147
5.12
Proportion of occupants’ adaptive behaviour in Indonesia, Japan, and Malaysia with 95% confidence interval error bars
149
Additional means to adapt with warm thermal discomfort in CL mode in Malaysia, indicated by red circles a) personal fan, b) portable fan
149
Adaptive behaviour of occupants in each ventilation mode in Indonesia with 95% confidence interval error bars
150
5.15
Opened windows in MM ventilation, Indonesia
151
5.16
Adaptive behaviour of occupants in each ventilation mode in Japan with 95% confidence interval error bars
152
Occupants in Japan adapted on thermal discomfort a) opening windows in FR mode, b) using personal fan, hand fan, and drinks in CL mode, indicated by red circles
152
Adaptive behaviour of occupants in CL mode in Singapore with 95% confidence interval error bars
153
5.6 5.7
5.8 5.9 5.10
5.13
5.14
5.17
5.18
xix
LIST OF SYMBOLS
a
-
Griffiths' constant
Ad
-
Dubois body surface area (m2)
AH
-
Absolute humidity (gv/kgda)
C
-
Convective heat loss per unit area (W/m2)
Cres
-
Dry respiration heat loss per unit body surface area (W/m2)
D
-
Diameter (m)
e
-
Euler’s number (2.718)
Ԑ
-
Emissivity
E
-
Evaporative heat loss per unit body surface area (W/m2)
Edif
-
Evaporative heat loss by diffusion through skin (W/m2)
Eres
-
Latent respiration heat loss (W/m2)
Esk
-
Evaporative heat loss from skin (W/m2)
Esw
-
Sweat evaporation heat loss (W/m2)
fcl
-
Clothing area factor
H
-
Metabolic heat production
h
-
Body height (m)
hc
-
Convective heat transfer coefficient (W/m2K)
Icl
-
Clothing insulation (clo)
K
-
Conductive heat loss per unit area (W/m2)
M
-
Metabolic rate (met)
n
-
Number of sample
pa
-
Partial pressure of water vapour in air (kPa)
pt
-
Total barometric pressure (mmHg)
pv
-
Partial pressure of water vapour (mmHg)
Qres
-
Total rate of heat loss through respiration (W/m2)
Qsk
-
Total rate of heat loss from skin (W/m2)
R
-
Radiative heat loss per unit area (W/m2)
xx Rcl
-
Total thermal resistance of clothing (m2 °C/W)
RH
-
Relative humidity (%)
S
-
Heat storage
Ta
-
Indoor air temperature (°C)
Tcg
-
Comfort globe temperature (°C)
Tci
-
Comfort air temperature (°C)
Tcl
-
Surface temperature of clothed body (°C)
Tcmrt
-
Comfort mean radiant temperature (°C)
Tcop
-
Comfort operative temperature (°C)
Tg
-
Indoor globe temperature (°C)
Tmrt
-
Indoor mean radiant temperature (°C)
To
-
Outdoor air temperature (°C)
Tod
-
Daily mean outdoor air temperature (°C)
Tpma
-
Prevailing mean outdoor air temperature (°C)
Tom
-
Monthly mean outdoor air temperature (°C)
Top
-
Indoor operative temperature (°C)
Trm
-
Running mean outdoor air temperature (°C)
Va
-
Air velocity (m/s)
W
-
Energy used for mechanical work
w
-
Body weight (kg)
xxi
LIST OF ABBREVIATIONS
AA
-
Air Movement Acceptance
AC
-
Air Conditioning System
ASHRAE
-
American Society of Heating, Refrigerating, and AirConditioning Engineers
AV
-
Air Movement Vote
CEN
-
Comité Européen de Normalisation (European Committee for Standardization)
CIBSE
-
Chartered Institution of Building Services Engineers
CL
-
Mechanical Cooling Ventilation
FR
-
Free-Running Ventilation
HP
-
Humidity Preference
HF
-
Humidity Feeling
HVAC
-
Heating, Ventilating, and Air Conditioning
ISO
-
International Standards Organization
MM
-
Mixed Mode Ventilation
PMV
-
Predicted Mean Vote
PPD
-
Predicted Percentage of Dissatisfied
S.D.
-
Standard Deviation
S.E.
-
Standard Error
SHASE
-
Society of Heating, Air-conditioning, and Sanitary Engineering of Japan
TA
-
Thermal Acceptance
TP
-
Thermal Preference
TSV
-
Thermal Sensation Vote
xxii
LIST OF APPENDICES
APPENDIX
TITLE
PAGE
A
Instrument testing results
176
B
Questionnaire translations
181
C
Additional demographic information
188
D
Surface temperature data
192
CHAPTER 1
INTRODUCTION
1.1
Introduction
This chapter introduces the main theme of this research. It explains the research background, which leads to problem statement as well as research objectives and research questions. It also discusses research scope, research significance to knowledge, and explains the structure of this thesis.
1.2
Research Background
The building sector, in contrast with other sectors, is the biggest energy consumer. Nearly half of the total electricity usage in buildings was for the sake of providing a thermally comfortable indoor condition, as reported from air-conditioning usage in tropical countries (Karyono and Bahri, 2005; Saidur, 2009; Suruhanjaya Tenaga Energy Commission, 2014). Since the buildings are exposed to solar radiation during day time all year round, most people in tropical climate generally prefer low temperature settings. During high outdoor temperature condition, energy consumption increases as lower temperature setting is used. This has led to “minimum temperature setting campaigns” in Japan, such as Cool Biz, promoting comfortable clothings in offices and minimum air conditioning system (AC) setting of 28°C for cooling (Haneda, 2010), while Malaysian government is promoting a minimum temperature setpoint of 24°C in its offices (Lau et al., 2009). However, building occupants’ comfort should be taken into account concurrently with these energy saving measures.
2 Thermal comfort is a key issue in sustainable buildings assessment. It is related to several aspects, such as environmental, social, and economic aspects. While Heating, Ventilating, and Air Conditioning (HVAC) system becomes a common solution to maintain comfort, its production, installation, operation, and maintenance use a lot of natural resources to manufacture and operate, which subsequently affects the environment, i.e. considerably contributes to CO2 emmission (Kharseh et al., 2014). From social point of view, thermal discomfort could lead to distress and health issues such as hypertension (Pimenta and Assunção, 2015). Exposure to excessively high or low temperatures might even result in deadly cardiovascular diseases, such as in the 2008 European heatwave which caused additional 70,000 deaths (Robine et al., 2008). From an economic perspective, understanding occupant’s comfort level will allow buildings to operate more efficiently and at lower cost by consuming less energy; for example by adjusting the usage of AC system, or even using operable windows instead of AC when the weather is within comfortable range (Karyono and Bahri, 2005; Rijal et al., 2007).
Comfort expectations of a population in certain climatic condition might differ from people who live in other climates. Based on previous field studies in Malaysia (Ahmed, 2003), India (Indraganti, 2011), Singapore (Yang et al., 2013), and United Kingdom (Humphreys et al., 2013), it was shown that temperatures well above 30°C are still considered within comfortable range in some cases, despite it normally being considered uncomfortable in many other places. In adaptive thermal comfort theory, the heat exchange calculations could not be strictly applied to all condition since human beings tend to adapt with their surroundings. Through adaptive behavior such as drinking hot or cold drinks, changing clothes, or using building openings e.g. windows or doors, people could overcome their thermal discomfort. This adaptive behavior theory is a plausible explanation about how people maintain comfort in relatively high indoor temperature settings, such as under ‘setsuden’ (saving electricity) condition in the Japanese summer (Indraganti et al., 2013).
Therefore, this research mainly aimed to investigate the thermal comfort of office building occupants in hot and humid condition in several countries: Malaysia, Indonesia, Singapore, and Japan, during summer season. It also analysed the
3 correlations of parameters affecting thermal comfort, as well as human behavior in terms of adapting with thermal environment. The results could be a useful reference for a better understanding of comfort temperatures in office buildings for architects, urban planners, and decision makers to achieve more efficient energy usage, especially in tropical countries.
1.3
Problem Statement
Most office buildings in tropical countries in Southeast Asia use mechanical cooling (CL) ventilation mode. There are also smaller numbers of free running (FR) and mixed-mode (MM) ventilated office buildings. However, some recent studies showed that some occupants feel cold inside the mechanically cooled buildings, in spite of the hot and humid weather outside (Sekhar, 2015). The evidence of overcooled building issues were shown through previous climate-chamber field studies in the hot humid weather of Hong Kong (Chan et al., 1998; Mui and Chan, 2003; Lai et al., 2009; Lee et al., 2012) and a field study in Singapore (Sekhar et al., 2002). Overcooled office buildings is a serious issue because of the resource consumption and energy waste. The increasing usage of HVAC system will further harm the environment, since the operation releases poisonous gases such as chlorofluorocarbons and hydrochlorofluorocarbons. This study hopes to provide comfort temperature range of people working in tropical climate as a feedback to ensure technology is used efficiently, meets society’s needs, and does not cause harm for environment.
According to heat-exchange theory, there are six factors affecting thermal comfort. Four of them are physical parameters: air temperature, mean radiant temperature, air velocity, relative humidity; while the other two are personal parameters: metabolic rates and clothing insulation (Fanger, 1970). In tropical climate and other hot-humid area, the high humidity could be an important issue to address in thermal comfort studies. Other than humidity, previous studies in other hot-humid weather shown that elevated air velocities could increase comfort temperature (Fong et al., 2010; Manu et al., 2014). Besides the mentioned six parameters, another individual characteristis such as demographic information might contribute to different
4 thermal response from people in tropical climate. Further studies about the effect of these parameters on thermal comfort is needed.
According to adaptive thermal comfort theory, people tend to adapt to their surrounding environment when they experience discomfort, using any available means around them. The adaptive behaviour could be different from one country to another, since it might be related to cultural and local conditions. In office buildings, the possible activities are generally restricted to those appropriate for working environment conditions. Meanwhile in Japan, the Cool Biz campaign has been applied in offices during summer season, which allow workers to dress casual, in return of using less air conditioning system for cooling purposes (Nakashima, 2013). This condition of Japan during summer season would be useful to investigate and compare with the tropical countries. A field survey about how people adapt to thermal discomfort in their working area in general could be invaluable information, especially for office building managements to help maintain the office workers’ comfort condition.
1.4
Research Objectives
Based on the problems mentioned in previous section, there are three main objectives of this study: i.
To investigate comfort temperature of office building occupants with different ventilation modes in hot and humid condition in Malaysia, Indonesia, Singapore, and Japan during summer season, then compare the results with related standards (i.e. ASHRAE Standard 55, ISO Standard EN 15251, and CIBSE guideline),
ii.
To analyse correlations of parameters which affect thermal comfort (such as humidity, air velocity, and demographics), as well as their effects on thermal comfort,
5 iii.
To reveal adaptive behaviour of office building occupants to maintain their thermal comfort.
1.5
Research Questions
Based on the objectives stated above, this study would look to answer three research questions: i. What is the comfortable temperature range for occupants in office buildings with differing ventilation modes; mechanical cooling, mixed mode, and free running in hot and humid condition? How does the result perform in comparison with international standards?
ii. What are the correlations of parameters that affect thermal comfort? How do humidity, air velocity, and demographic affect thermal comfort?
iii. How is the behaviour of office building occupants in maintaining their thermal comfort?
1.6
Research Scope and Limitation
The scope of this study is comfort temperatures, the factors affecting thermal comfort, respondents’ demographics, and specific behavior or actions that are performed on purpose to overcome thermal discomfort. The scope of field investigation are office spaces in 13 buildings in four countries: Malaysia, Indonesia, Singapore, and Japan. The investigated offices are not necessarily the whole building, but only selected rooms of office are taken as samples. This study does not cover the specification of air-conditioning machine used in each buildings, energy usage, building illuminance, and neither indoor air quality of the investigated buildings.
6 1.7
Research Significance
For several decades, numerous research about thermal comfort had been conducted. Various methodology were used, both simulation-based and field study. Contemporary research on thermal comfort were mostly conducted using computational simulations, using artificial built environment models where the parameters condition could be easily set up according to experimental requirements. In order to make the results applicable and reliable, regularly updated field measurements on real conditions are needed to validate simulation results (Brager and de Dear, 1998). This research would contribute on real-life field measurement studies based on adaptive comfort theory in four tropical climate countries and one temperate climate country, Japan, during summer season; taken from samples of office buildings which are still operating today and expected to be specifically beneficial over the next 20 years of these buildings’ lifespan.
1.8
Thesis Structure
This section summarises how this thesis is organised and provides an overview of each chapter, as detailed below.
Chapter 1 includes the research background, problem statement, and objectives of this study. This chapter also provides research scope, significance of this study and this section on the organisation of the thesis.
Chapter 2 reviews basic theory of thermal comfort, started from heat balance equation until the recent adaptive comfort theory. This chapter also reviews previous studies in both tropical and temperate climates. The reviews are then filtered based on analytical method used in obtaining comfort temperature, which are regression analysis, probit analysis, and Griffith’s method. This chapter explains the factors affecting thermal comfort, building ventilation modes, and thermal comfort standards.
7 Chapter 3 explains research methodology used in this study. The first section presents overall structure of methodology. The next section describes the climate and geography of each location where field studies were conducted, followed by information about investigated office buildings. The rest of sections in this chapter provide data collection methods and analytical techniques.
Chapter 4 presents all primary data results in the study, demarcated into descriptive measures, field measurement results and questionnaire survey results. Descriptive measures includes sample size, socio-demographic data of respondents, as well as clothing insulation values and metabolic rate. Results of field measurement are physical thermal parameters; namely outdoor air temperature, four indoor thermal indices, relative humidity, and air velocity. Results of questionnaire survey are data taken directly from respondents, which include occupants perception on their thermal environment and predicted mean vote.
Chapter 5 provides analysis of all the results from previous chapter, as well as discussions on the results of each analysis. The first section is analysis on comfort temperature, which is obtained through three analytical methods: regression, probit, and Griffith’s method. The second section analyses the factors affecting thermal comfort, presented in three subsections: air velocity, humidity, and demographics. The third and last section of this chapter is analysis on adaptive behaviour of office building occupants towards thermal discomfort.
Chapter 6 presents concluding remarks and further recommendations. Research conclusion section includes comfort temperatures, factors affecting thermal comfort, and adaptive behaviour. The last section provides recommendation, limitations of this study, as well as potential future research.
1.9
Chapter Summary
This chapter introduced the present study by explaining the research background of thermal comfort studies, building up problem statement, and then
8 stating the objectives of the research based on the problems. This was followed by research questions, scope, significance, and lastly the organisation of this thesis. To understand the basic theory and current progress on thermal comfort studies, literature review is presented in the next chapter.
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
This chapter reviews past literatures relevant to this study. It starts with basic theory of thermal comfort, which is mainly explaining about heat balance theory and the recent adaptive theory. It is followed by explanation about factors affecting thermal comfort, both indoor thermal environment and personal parameters. Afterwards, field studies in office buildings are reviewed, demarcated into studies in tropical climate countries and Japan. Each building ventilation modes which is part of case study in this research are defined in the next section, then the last section presents both international standards and local guidelines about thermal comfort in each investigated location.
2.2
Basic Theory
Thermal comfort is defined as ‘that condition of mind which expresses satisfaction with the thermal environment’ (ASHRAE, 2013; European Committee for Standardization (CEN), 2005). This definition is useful as it preempts any semantical arguments. This definition also emphasises the psychological component of thermal comfort, separate from the physical environment or physiological state.
10 The reasoning behind feelings of thermal comfort, warmth, pleasure, or freshness, is complex and not completely knowable. The effects of the thermal environment on these feelings and the inability to achieve thermal comfort are demonstrated when occupants complain, have lower productivity and morale, and can outright refuse to work in a thermally uncomfortable environment. This pragmatism has driven the research of thermal comfort and the conditions where it can be achieved.
Thermal comfort research has always sought to find the conditions with acceptable thermal environments and where occupants experience thermal comfort, but have skimped on understanding the reasoning behind thermal comfort and discomfort. The research questions are mostly about what kind of indoor thermal environment (consisting of air temperature (Ta), mean radiant temperature (Tmrt), air velocity (Va), and relative humidity (RH)) will provide comfort for certain user conditions (clothing insolation (Icl) and metabolic rate (M)), and the effects of deviating from those condition, in terms of thermal comfort.
2.2.1
Heat balance equation Internal human body temperature should be maintained around 37°C
(Campbell, 1987). This implies that there is heat balance between the body and its environment; on average, heat transfer into and heat generated inside the body are balanced by heat output from the body.
However, this does not imply a steady state with unchanging temperatures. Instead, there is a dynamic balance; body temperature will rise if heat outputs are less than heat generation and inputs, and vice versa. The body heat balance is related to both heat generation as well as heat loss through radiation, convection, and evaporation, contributing to 45%, 35%, and 20% of heat loss respectively (Baker and Steemers, 2000). While there are many equations that represent heat balance within the human body, all stem from the same concept of using variables for internal heat generation, heat transfer, and heat storage, as shown in conceptual heat balance Equation 2.1 (Parsons, 2003):
11 𝑀−𝑊 =𝐸+𝑅+𝐶+𝐾+𝑆
(2.1)
where 𝑀 is the metabolic rate of the body, 𝑊 is the energy used for mechanical work. The left term of Equation 2.1, 𝑀 − 𝑊, relates to the metabolic energy not used for work, thus released as heat. 𝑆 is the rate for heat storage. All other terms relate to heat transfer, i.e. via conduction 𝐾, convection 𝐶, radiation 𝑅, and evaporation 𝐸.
To achieve constant body temperatures, the rate of heat storage 𝑆 should be equal to zero. Conversely, if 𝑆 > 0, there is net heat gain, therefore body temperature will rise, and vice versa. To achieve heat balance, the following equation must hold true:
𝑀−𝑊−𝐸−𝑅−𝐶−𝐾 = 0
(2.2)
where 𝑀 − 𝑊 is always positive; while 𝐸, 𝑅, 𝐶 and 𝐾 are rates of heat loss from the body (i.e. positive value is heat loss, negative value is heat gain). All the terms used in these equations are expressed as rates of heat production or losses, in Joules per second (J/s) or watts (W) and allows for simple additions for heat gains and losses. To account for different body sizes, a further term for body surface area is appended and rates of heat gains or losses over body surface area expressed as W/m2 . The relation of heat balance and thermal comfort was defined:
Thermal comfort depends on the heat physiology of the person: A person must keep his core body temperature constant and therefore has to be able to transfer the excess heat produced by his metabolism into the surroundings. (Hausladen et al., 2005)
To quantify the components in this equation and make it practically useful for an analysis of heat exchange, it is important to identify the specific channels for heat production and exchange in the human body and determine the equations to calculate
12 these heat production and exchange processes. These terms must also be measurable in order to be applicable. Fanger (1970) uses the heat balance equation:
𝐻 − 𝐸𝑑𝑖𝑓 − 𝐸𝑠𝑤 − 𝐸𝑟𝑒𝑠 − 𝐶𝑟𝑒𝑠 = 𝑅 + 𝐶
(2.3)
where H is metabolic heat production, Edif is heat loss through skin vapour diffusion, Esw is heat loss through sweat evaporation, Eres is heat loss through latent respiration, Cres is dry respiration heat loss, R is clothing surface heat radiation, and C is clothing surface heat convection.
Heat respiration through conduction is normally assumed to be negligible. This is addressed in the heat balance equation from American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE, 1989):
𝑀 − 𝑊 = 𝑄𝑠𝑘 + 𝑄𝑟𝑒𝑠 = (𝐶 + 𝑅 + 𝐸𝑠𝑘 ) + (𝐶𝑟𝑒𝑠 + 𝐸𝑟𝑒𝑠 )
(2.4)
where Qsk is the total rate of heat loss from the skin, Qres is total rate of heat loss through respiration, Esk is the evaporative heat loss rate from skin and all other terms follow previous definitions. It should also be noted that the Esk is equivalent to the combined rate of evaporative heat loss though sweating (Esw) and evaporative heat loss through skin from moisture diffusion (Edif), as expressed in Equation 2.5.
𝐸𝑠𝑘 = 𝐸𝑠𝑤 + 𝐸𝑑𝑖𝑓
(2.5)
Practically, to quantify heat balance equations, one should consider body heat production (M – W), skin surface heat loss (C + R + Esk ), and respiratory heat loss (Cres + Eres ). To be within thermal comfort, a person should have a body in balanced heat, sweat rate, and mean skin temperature that is within their comfort range, and no local thermal discomfort (Fanger, 1970).
13 2.2.2
Predicted Mean Vote (PMV) and Predicted Precentage of Dissatisfied (PPD) There is bipolarity in thermal sensation and thermal comfort, since the
sensations of comfort lie between the extremes of being uncomfortably hot or cold. From a steady state perspective, thermal comfort can instead be viewed as a lack of discomfort. This notion does not hold as some experiences of thermal pleasure, such as when transferring from a cold to a warm thermal environment, are transient. Therefore, thermally acceptable conditions are described as subjects’ average subjective expression, based on a scale of thermal sensations.
Human sensation to their thermal surroundings is always a subjective matter. The attempt to quantify this qualitative data was one of thermal comfort fundamentals; there are two kinds of thermal sensation scale which are known internationally, ASHRAE scale and Bedford scale, written as seven-point scales. Fanger (1970) has found a mathematical way to estimate the average vote of a group of people, given the seven-point sensation vote, in a specific thermal environment condition. As long as the basic six variables (Ta, Tmrt, RH, Va, Icl, M) are known, the average vote which represents 80% of the sample could be predicted.
This prediction is possible since the degree of thermal discomfort depends on thermal load, which is the difference between the heat loss from human skin and the internal heat production. Hypothetically, this deviation is stored at the comfort values, based on mean skin temperature and sweat secretion on certain activity level. When there is no deviation or zero thermal load, people are in the thermal comfort state. In contrast, people with imbalance function of body thermal load and activity level will experience discomfort (Fanger, 1970).
There is data for sedentary metabolic heat generation from previous thermal comfort studies by Nevins et al. (1966) and Fanger (1970). McNall et al. (1968) provides data for four activity levels, based on a study of 1396 subjects. These studies allow for the Equation 2.6 for predicted mean vote (PMV) in large groups of subjects.
14
𝑃𝑀𝑉 = (0.303𝑒 −0.036𝑀 + 0.028){(𝑀 − 𝑊) − 3.96𝐸 −8 𝑓𝑐𝑙 [(𝑇𝑐𝑙 + 273)4 − (𝑇𝑚𝑟𝑡 + 273)4 ] − 𝑓𝑐𝑙 ℎ𝑐 (𝑇𝑐𝑙 − 𝑇𝑎 )
(2.6)
− 3.05[5.73 − 0.007(𝑀 − 𝑊) − 𝑝𝑎 ] − 0.42[(𝑀 − 𝑊) − 58.15] − 0.0173𝑀(5.87 − 𝑝𝑎 ) − 0.0014𝑀(34 − 𝑇𝑎 )}
where e is the Euler’s number, 2.718. M is metabolic rate in W/m2. W is external work, assumed as zero. fcl is clothing factor, estimated by Equation 2.7. Tcl is surface temperature of clothing in °C, it is estimated with Equation 2.8. Tmrt is mean radiant temperature in °C. hc is convective heat transfer coefficient, estimated by Equation 2.9, based on Va, air velocity in m/s. Ta is air temperature in °C. pa is vapour pressure of air in kPa.
𝑓𝑐𝑙 = 1.0 + 0.2𝐼𝑐𝑙
(2.7)
𝑇𝑐𝑙 = 35.7 − 0.0275(𝑀 − 𝑊) − 𝑅𝑐𝑙 {(𝑀 − 𝑊) − 3.05[5.73 − 0.007(𝑀 − 𝑊) − 𝑝𝑎 ] − 0.42[(𝑀 − 𝑊) − 58.15] − 0.0173𝑀(5.87 − 𝑝𝑎 )
(2.8)
− 0.0014𝑀(34 − 𝑇𝑎 )}
ℎ𝑐 = 12.1(𝑉𝑎 )1/2
(2.9)
where Rcl is clothing thermal insulation, estimated by Equation 2.10, based on Icl, clothing insulation in clo unit.
𝑅𝑐𝑙 = 0.155𝐼𝑐𝑙
(2.10)
Predicted percentage of dissatisfied (PPD) models the number of potential complaints due to thermal discomfort. The relationship between PPD and PMV was provided in previous research dataset (Nevins et al., 1966; Rohles, 1970; Fanger, 1970), as seen in Equation 2.11 (ISO 7730, 1994) and Figure 2.1. Fanger (1970)
15 derived practical application for PMV and PPD, using it to analyse the conditions of an indoor space and finding specific areas that need specific attention using a thermal non-uniformity index, changing these conditions to achieve the lowest possible percentage of dissatisfied.
Predicted percentage of dissatisfied, PPD (%)
𝑃𝑃𝐷 = 100 − 95𝑒 [−(0.3353𝑃𝑀𝑉
4 +0.2179𝑃𝑀𝑉 2 )]
(2.11)
100 80 60 40 20 0 -3
-2
-1
0
1
2
3
Predicted mean vote, PMV
Figure 2.1 The predicted percentage of dissatisfied (PPD) as a function of the predicted mean vote (PMV) index
2.2.3
Adaptive Thermal Comfort The adaptive approach is defined as people’s reactions to changes that produce
discomfort, in order to restore their comfort (Nicol and Humphreys, 2002). The adaptive thermal comfort model thus interprets this inclination to maintain comfort by changing or adapting to their environment.
Another approach was the climate chamber study, which subjects respondents to an environment where environmental parameters; air temperature, radiant temperature, humidity, and air velocity can be controlled. A comparative study of thermal comfort between occupants in a climate chamber against their homes and offices in winter by Oseland (1997) demonstrated, under steady state conditions, comfort temperatures in occupants’ homes and offices are 2.2 °C and 0.7 °C lower than in the climate chamber. The mentioned author attributes this to having greater control on adaptive opportunities and temperature.
16 Field studies have shown that occupants’ thermal responses, in naturally conditioned spaces controlled by occupant, depend in part on outdoor climate and may vary from thermal responses in buildings with centralized HVAC systems; primarily because of the various thermal sensations, clothing changes, controllability, and changes in building users’ expectations. This finding then became definition of ‘adaptive model of thermal comfort’ according to ASHRAE Standard 55 (2010).
In this model, it is assumed that when a change that causes thermal discomfort occurs, people will attempt to restore their comfort by reacting to this change (Auliciems, 1983). People will not passively accept discomfort and will form adaptive behaviours to stay or become comfortable. This is known as behavioural thermoregulation where people move to more comfortable locations, adjust their clothing, or act in other ways to ensure their survival, comfort, and performance.
There is dynamic interaction between people and their thermal environment, but there is little account for human behaviour in assessing thermal comfort. The reason for this gap may stem from the dynamicism present in the thermal environment, which is markedly different from the climate chambers that is the basis of PMV studies (Hensen, 1990). The early study of adaptive thermal comfort stems from previous works in Australia (Auliciems, 1981) and the United Kingdom (Humphreys, 1976).
This field of thermal comfort has gained international attention from standard makers such as ASHRAE and the ISO, which in turn lends credibility to adaptive thermal comfort. The use of adaptive modelling is important because of the new opportunies to further understand the human element in thermal comfort. While current non-adaptive standards and models are convenient to use, they do not add to the understanding or application of thermal comfort.
ASHRAE has sponsored a series of field studies into adaptive thermal comfort models, to support the Standard 55 (ASHRAE, 2013). The WinComf software (Fountain and Huizenga, 1996) is the product of earlier studies comparing the PMV (Fanger, 1970) with the effective temperature indices (Gagge et al., 1971). This series also included the landmark studies on the development of global thermal comfort
17 experiment database, the RP-884 research project sponsored by ASHRAE (de Dear, 1998) and the adaptive model for thermal comfort and preferences (de Dear and Brager, 1998).
The former, the global database of thermal comfort studies (de Dear, 1998) contained 21,000 sets of raw data from 160 buildings collected by research groups around the world, divided into classes denoting the quality of data. This database formed the basis of further analyses (Humphreys and Nicol, 2002) as well as the foundation of the adaptive model of thermal comfort (de Dear and Brager, 1998).
While the PMV index makes accurate predictions in buildings with centralised HVAC system, occupants’ thermal sensation in buildings with natural ventilation differs from those predicted with the index (de Dear and Brager, 2002). Furthermore, the work by Nicol and Humphreys (1973) demonstrated different comfort tempertures for different groups globally. This was developed further into a regression of comfort temperatures based on the mean monthly outdoor temperatures, an average of the month’s daily maximum and minimum temperatures (Humphreys, 1976).
This was followed by the reanalysis of the ASHRAE database by regressing exponentially averaged mean outdoor temperature against indoor comfort temperature, that has demonstrated people from warmer climates prefer warmer temperatures and vice versa (Oseland and Humphreys, 1994). Further field studies have developed newer models of comfort temperatures that use regressions against exponential averages of mean outdoor temperatures (Humphreys et al., 2007; Nicol and Humphreys, 2010; Toe and Kubota, 2013), which is important as it takes into account occupants’ experience over previous days. Using predictive algorithms in lieu of traditional control methods, it is possible to maintain thermal comfort while lowering energy costs, as used in the United Kingdom and Sweden (McCartney and Nicol, 2002).
Oseland (1997) discovered that the discrepancy of comfort votes between homes, offices, and the climate chamber are cased by poor estimation of metabolic rates and suggest prescribing temperature ranges is the more relevant approach in
18 ensuring occupants’ thermal comfort. A further study by Oseland et al. (1998) attempts to quantify the effets of adaptive behaviour in achieving comfort by relating outside conditions to indoor comfort temperatures, which explains the former variation in comfort temperatures.
Thermal adaptation can be grouped into three categories; behavioural, physiological, and psychological, where behavioural adaptation are further divided into personal, technological, and cultural behaviours (de Dear and Brager, 1998). Physiological adaptation that provide acclimatisation benefits such as better heat tolerance (Gonzalez, 1979) have been found in some studies to not affect occupants requirements for thermal comfort (Brierley, 1996; Parsons, 2002). Psychological adaptions result in sensory information that are altered based on past experience, which then result in similarly altered reactions to this information (de Dear and Brager, 1998).
Of these three categories, it has been found that thermal comfort is best maintained through behavioural adjustment (Williams, 1996). Adaptive opportunity is the extent to which the environment allows for these adjustments (Baker and Standeven, 1996). The perception of having adaptive opportunities have been found to be as important as having actual adaptive opportunities (Pacink, 1990). This perception is also cited as the explanation for differences between PMV and thermal comfort votes in buildings without HVAC (de Dear and Brager, 1998). To compensate for this and bring PMV in line with actual thermal sensation votes, Fanger and Toftum (2002) introduced the expectancy factor, e, where PMV is multiplied by this factor based on the level of expectancy; e = 0.5 for low expectancy and e = 1.0 for high expectancy.
Based on adaptive thermal comfort approach, adaptive field studies are formed. Unlike climate chamber setting, where independent variables such as temperature are manipulated directly, the adaptive field studies leave thermal environment variables as it is on default condition, while minimizing any intervention. The latter method is used in this study, since the thermal environment is a self-regulating system, which both influences occupants’ perception and is modified by adaptive behaviour (Humphreys, 1976b). By using laboratory-based settings, this system might be interrupted.
19 2.3
Factors Affecting Thermal Comfort
Fanger (1970) defined thermal comfort to be measured within the dimension of the six basic parameters, consisting of four environmental factors: air temperature, radiant temperature, humidity, and air velocity, as well as two personal factors: metabolic rate and clothing insulation. A seventh factor, individual characteristics, includes demographic information and expectations. The latter refers to occupants’ expectation of a building’s comfort level based on their perceptions and attitudes (McDowall, 2007).
In both hot and cold environments, humans essentially respond to the interactions of these factors. The combined effect of air velocity and metabolic rate was demonstrated by Collins (1983) using the analogy of a skier carrying a child: the skier overcomes heat loss from low temperatures and high air velocity with high metabolic heat generation while the child, inactive, is unable to compensate. Humidity and clothing permeability are important components in evaporative heat loss through sweating (Parsons, 2003). These findings indicate the importance of understanding the effects from interaction between the parameters affecting thermal comfort.
2.3.1
Indoor air and radiant temperatures Temperature, the average kinetic energy in a body, obeys the laws of
thermodynamics where heat flows from bodies with higher temperature to those with lower temperatures. Humans are homeotherms and will attempt to regulate their body temperature at around 37 °C (Parsons, 2003). Because the human body, in practice, is surrounded by air, heat transfer out of the human body is mostly driven by air temperature.
Air temperature is defined in Parsons (2003) as ‘the temperature of the air surrounding the human body which is representative of that aspect of the surroundings which determines heat flow between the human body and the air’. Because of the homeothermic nature of humans as well as the dynamic nature of climatic conditions,
20 the temperature of air surrounding a human body will always vary. This will in turn constantly affect thermal sensations, as the heat exchange rates fluctuate.
Another factor that influences thermal comfort, related to air temperature, is radiant temperature. Heat exchange through radiation happens between all bodies with a temperature differential, even through vacuum. Because approximately 45% of heat loss from the human body happens through radiation (Baker and Steemers, 2000), this is an important factor to consider for thermal comfort. Radiant temperatures are related to the concept of radiation fields, where at any point within this field there are dynamic exchange of heat energy via radiation through time and space (McIntyre, 1980).
Thermal radiation is part of the electromagnetic spectrum and can be conceptually related to light, which allow the definition of radiant temperatures as ‘the temperature of a black-body source that would give the same value of some measured quantity of the radiation field as exists in reality’ (McIntyre, 1980). Parsons (2003) further defines mean radiant temperatures as ‘the temperature of a uniform enclosure with which a small black sphere at the test point would have the same radiation exchange as it does with the real environment’. A sphere is used because its measurements do not depend on its orientation.
2.3.2
Air velocity Heat flow to and from a human body can be influenced by air movement, which
in turn influences body temperature. In practical terms in thermal comfort studies, mean air velocity, which measures the integrated air velocity over all directions, is used. Parsons (2003) writes that the mean air velocity is useful as it represents the rate at which warm air or vapour flows away from the body. Field studies have shown that increasing air flow speeds around occupants will boost the evaporative heat loss from skin (Szokolay, 1987). This is especially important in hot and humid climates where evaporative heat loss is severely restricted by air humidity (Szokolay, 1985). This is also important due to the proportion of bodily heat loss from evaporation (Baker and Steemers, 2000).
21 In a more recent study consisting of 7,000 observations in 27 naturally ventilated buildings in Hong Kong, it was shown that by varying air velocity, it is possible to increase comfort temperatures without varying other factors and achieve 80% PPD at temperatures of 29.5 °C with 0.3 m/s air velocity (Cheng and Ng, 2006). Studies in other countries such as Malaysia (Salleh, 1989) and India (Manu et al., 2014) also found that high air velocity was effective to use as adaptive measure to maintain thermal comfort during high temperature conditions. The ASHRAE Standard 55-2013 included the range of comfort operative temperature changes in terms of different air velocity level, as shown in Figure 2.2.
Figure 2.2 2013)
2.3.3
Comfort operative temperature in different air speed level (ASHRAE,
Humidity Heat transfer via evaporation works by having water or sweat that is heated up
by body evaporation, thereby transferring heat into the surrounding environment and cooling down the body. This action of vapour transfer is driven by the difference in absolute humidity between the immediate environment of the body surface (Parsons, 2003).
22 Environmental humidity is usually expressed as either relative humidity or partial vapour pressure. Partial vapour pressure, expressed as pa in N/m2 units, is the prevailing partial pressure of water vapour in the air and when expressed as a percentage of saturated water vapour pressure, this becomes the more commonly used relative humidity (Parsons, 2003). It is also possible to connect partial vapour pressure to absolute humidity as a function of temperature using common gas laws (Kerslake, 1972).
Humidity is important as a determinant for evaporative heat loss due to the partial pressure exerted by evaporating water vapour. As more vapour is transferred into the air, more pressure will be exerted and it will eventually reach a maximum, the saturated vapour pressure, when relative humidity is 100%. Saturated water vapour pressure is related to air temperature, where higher air temperatures can contain higher saturated water vapour pressure. Therefore, saturated water vapour pressure is also related to its dew point, which is the temperature where dew would form if the air temperature slowly dropped (Kerslake, 1972).
However, the effects of humidity on thermal comfort is less pronounced in newer studies. Given a fixed clothing value of 0.6 clo and identical activity levels, de Dear et al. (1991) found that subjects could not discern the difference between relative humidities set at either 35% or 70%. Within relative humidities of 30-70%, Kuchen and Fisch (2009) also showed that no significant change in thermal comfort was experienced by subjects as relative humidity changed.
2.3.4
Clothing Insulation The role of clothing is an important aspect in studying thermal comfort. Using
heat loss through radiation as an example, Parsons (2003) had emphasised the importance of clothing, including its ventilation rate, in determining heat transfer from the body into the environment. The effect of clothing insulation can then be included in the heat balance equation to produce results that take into account subjects’ clothing.
23 According to ASHRAE (2005), clothing insulation value (Icl) is expressed in clo units, which is equal to 0.155 Km2/W. This clo unit has the same dimension as the thermal resistance (R-value) used to measure insulation in building construction industry. 1 clo is equal to 0.88 R. An example of clothing ensemble for women and men, expressed in clo units, is shown in Table 2.1.
Table 2.1 : Sample of clothing insulation ensemble (ASHRAE, 2013) Garment description – Women
Icl (clo)
Garment description - Men
Icl (clo)
Bra and panties
0.04
Men’s brief
0.04
Stockings
0.02
Shoes
0.02
Shoes
0.02
Calf-length socks
0.03
Short-sleeve dress shirt
0.19
Short-sleeve dress shirt
0.19
Skirt (knee-length thin)
0.14
Straight trousers (thin)
0.15
Total
0.41
Total
0.43
Thermal adaptation through clothing is the primary method for building occupants to improve their thermal comfort. A study of occupants in seven buildings in France by Baker and Standeven (1996) revealed that occupants change their clothing based on the expectations of indoor conditions, which is estimated based on outdoor climate, along with thermal expectations and experience. This study also found that 28 occupants adjusted their clothing 62 times in 864 survey hours. Adaptive clothing behaviour was also shown in a study in Tunisia where occupants switch to heavier clothing in the desert to shield solar radiation and hot air (Bouden and Ghrab, 2005). This further reinforces the finding that behavioural adjustment is the best method to maintain thermal comfort (Williams, 1996).
Thermal adaptation through clothing adjustment is also important as a measure for saving energy, as demonstrated in findings in computer simulations by Newsham (1997), where occupants given the flexibility to choose their own clothes can maintain comfort temperatures amongst 90% of occupants while simultaneously reducing energy consumption by 41%.
24 2.3.5
Metabolic rates The human body produces heat constantly through metabolism, the breakdown
of nutrients into energy. This process produces heat and in order to maintain homoestatis, this heat must be emitted out of the body. Different levels of activity will produce different levels of heat. More vigourous activities will inevitably produce more heat that needs to be channelled into the environment. The amount of heat an activity produces is expressed in watts (W). However, because of differing human body shapes and sizes, it is not possible to use this heat production as is to measure thermal comfort.
The heat produced by a person is transmitted into the atmosphere through radiation, convection, and evaporation (Baker and Steemers, 2000). These processes are intimately tied to a person’s amount of body surface area. By incorporating body surface area into calculations for body heat exchange, it is possible to standardise this for people of all body shapes. The standard unit for metabolic activity is the ‘met’, which is equivalent to 58.2 W/m2, assuming a mean human body surface area of 1.7 m2. Based on this assumption, the metabolic rates for common activities have been tabulated by ASHRAE (2013) in Table 2.2.
25
Table 2.2 : Metabolic rates for common activities (ASHRAE, 2013) Activity Resting Sleeping Reclining Seated, quiet Standing, relaxed Walking (on level surface) 0.9 m/s 1.2 m/s 1.8 m/s Office activities Reading, seated Writing Typing Filing, seated Filing, standing Walking about Lifting/packing Miscellaneous Occupational Cooking House cleaning Seated, heavy limb movement Machine work sawing (table saw) light (electrical industry) heavy Handling 50 kg (100 lb) bags Pick and shovel work
2.3.6
Metabolic Rate (met)
Metabolic Rate (W/m2)
0.7 0.8 1.0 1.2
40 45 60 70
2.0 2.6 3.8
115 150 220
1.0 1.0 1.1 1.2 1.4 1.7 2.1
55 60 65 70 80 100 120
1.6-2.0 2.0-3.4 2.2
95-115 115-200 130
1.8 2.0-2.4 4.0 4.0 4.0-4.8
105 115-140 235 235 235-280
Demographics Along with the six basic parameters, it is also useful to look at occupants’
demographics, which may relate to the seventh factor; individual characteristics (McDowall, 2007). This study specifically looks at three demographic factors: local adaptability using the geographical-locational factor, differences between genders, and body surface area.
26 Intuitively, it is believed that different conditions for thermal comfort is required for people living in different parts of the world. Parsons (2003) disagrees with this analysis because thermal comfort excludes the study of heat or cold stress, which may be influenced by acclimatisation, and that acclimatisation is completely ensconced by specific location, as well as adaptations in clothing and activity. This notion is reinforced by studies such as Baker and Standeven (1994) and Bouden and Ghrab (2005) where occupants adapt their behaviour based on local conditions.
Previous studies have found no difference in comfort temperatures in a given location between populations from different parts of the world. Nevins et al. (1966) found no significant difference in temperature preferences between Danish and American subjects. Similarly, Ellis (1953) showed that European and Asians in Singapore had a similar comfort temperature of 27 °C. Using this data, Fanger (1970) predicted a comfort temperature of 27.4 °C using the PMV comfort equation. These studies demonstrate that given similar environmental conditions, people will have similar comfort temperatures regardless of geographical origins.
Latter studies disagreed with these results. In China, Humphreys (1978) has shown a wider comfort temperature range, from 28.1 °C in Shanghai in summer to 18.5 °C in Beijing in winter. Auliciems and Szokolay (1997) argued that this occurs due to the vasomotor and evaporative mechanism adapatations that happen as subjects are exposed to new environmental conditions, with stress stimulus resulting in the adjustment of temperature preferences. Givoni (1998) attributes this to acclimatisation, where people living in a particular climate would tolerate local climatic conditions better, due to occupant experience and cultural context. This was shown in a study by de Dear and Leow (1990), where 583 occupants from 214 flats in Singapore have comfort temperatures 2 °C higher than those recommended by the PMV index and the ISO standard. Zainal (1993) also attributes the difference in Malaysian factory workers’ comfort temperatures that was 4 °C higher than a corresponding climate chamber study to their acclimatisation factor.
This acclimatisation factor also makes it difficult to make thermal comfort prediction across locations, with each study proposing different regressions
27 (Auliciems and de Dear, 1986; Nicol and Roaf, 1996; de Dear and Brager, 1998; Nicol et al., 1999). It is clear from past literature that thermal comfort studies have moved from ignoring acclimatisation towards acknowledging its effects in determining occupants thermal comfort.
In terms of gender, early results in thermal comfort studies indicate that there are no significant difference in comfort temperatures between male and female occupants. Despite this, women have been shown to be more sensitive towards any deviations from neutral temperatures (Fanger, 1970).
A comparative study between male and female occupants in a climate chamber environment with varying temperatures by Parsons (2002) demonstrated that both genders report similar thermal sensation in warm environments but females expressed greater dissatisfaction in cold environments, which is attributed to greater female physiological and psychological sensitivity to stimuli.
From a climate chamber study of 16 male and 16 female subjects, where temperatures were varied but humidity, air movement, activity levels, and clothing insulation were fixed, Breslin (1995) found that there are few gender differences in neutral and warm conditions, but females reported feeling colder in cool conditions. This study also showed that when analysing local sensations, females reported feeling colder in their hands. Another climate chamber study by Webb and Parsons (1997) with eight male and eight female subjects resulted in females reporting cooler sensation than males in cool conditions. In a field study, Cena and de Dear (2001) found that as temperatures move away from neutral, females tend to report more extreme sensations.
Some of this difference can be explained by physiological differences between genders. It has been found that women have lower metabolic rates than men, thus preferring warmer ambient temperatures (Koenigsberger et al., 1974; Auliciems and Szokolay, 1997). Additionally, females also experience lower evaporation heat loss from their skin surface (Kimura et al., 1994).
28 Parsons (2003) attributes some of the differences in thermal sensation between genders to derive from the difference in clothing. Olesen (2000) found some difference in clothing levels between males and females in the same building, which contributed to female occupants preferring temperatures of 23.0-24.0 °C while wearing 0.5 clo of clothing insulation, in contrast to male occupants prefering 20.0-24.0 °C wearing 1 clo worth of clothing insulation. Nakano et al. (2002) also found major differences between male and female clothing insulation, although suggested that it does not fully explain the difference in temperature preferences between genders.
2.4
Field Studies in Office Buildings
In office building, which is often owned by profit-entities organization or even mixed with commercial functions, thermal comfort is closely related to energy consumption in buildings, since HVAC system consumed around half of total electricity in a building (Karyono and Bahri, 2005; Saidur, 2009; Suruhanjaya Tenaga Energy Commission, 2014). Numerous thermal comfort research has been conducted all over the globe (de Dear and Brager, 2002; Lee et al., 2012b; Tanabe et al., 2013), with hope that energy use could be reduced by understanding the range of occupant’s thermal comfort better. Recent situation in developing countries with tropical climates has been worse, since AC system is popular for a means to overcome hot weather; the number of AC unit ownership is increasing dramatically for the last decade (Mahlia et al., 2001). This section delves into the studies in hot humid climates where AC usage is most prevalent to understand the conditions for thermal comfort in these climate conditions.
2.4.1
Field studies in tropical climate Past studies across the globe have invariably produced different equations to
predict thermal comfort (Auliciems and de Dear, 1986; Nicol and Roaf, 1996; Nicol et al., 1999; de Dear and Brager, 1998). This lack of universality points to the fact that thermal comfort is based on local climate. Therefore, local studies are important in understanding thermal comfort in hot humid climates.
29 Most studies of occupant’s thermal comfort in hot humid climates agree that the comfort temperatures found are higher than those recommended by international standards. A study by de Dear et al. (1991) in Singapore found that naturally ventilated and air conditioned buildings have upper comfort temperature thresholds of 31.0 °C and 26.7 °C respectively. A field study in Jakarta, Indonesia of seven office buildings, including one naturally ventilated and one with mixed mode ventilation by Karyono (2000) showed neutral temperatures of 26.4 °C. Studies in Bangkok, Thailand by Busch (1992) and Jitkhajornwanich et al. (1998) showed comfort temperatures upper limits of 31.0 °C and 31.5 °C respectively. In the summer in Guangzhou, China, with outdoor temperatures of 28.8 to 34.6 °C, occupants demonstrate upper temperature boundary of 30.5 °C (Zhang et al., 2010). These results demonstrate comfort temperatures higher than those recommended in thermal comfort standards.
Exceptions include Foo and Phoon (1986), where results show comfort temperatures below 24.0 °C, well below recommendations in international standards, but was attributed to occupants’ high clothing insulation values. This was also found in Ismail and Barber (2001), where 11 Malaysian CL offices showed occupant comfort temperatures between 20.8 to 28.6 °C, in comparison to indoor temperatures of 23.1 °C. Both these cases demonstrate significant overcooling, deviation from thermal comfort standards, and energy waste.
Other factors also affect comfort temperatures in hot humid climates. A study in Thailand by Khedari et al. (2000) of 288 subjects in naturally ventilated buildings with varying air velocities demonstrated that as air movement increases and relative humidity decreases, comfort temperature also increases, as shown in Figure 2.3. This finding was confirmed by Cheng and Ng (2006), where after 7,000 observations in 27 naturally ventilated buildings in Singapore, it is concluded that ceteris paribus, it is possible to increase comfort temperatures by increasing air velocity.
30
Dry bulb temperature (C)
Figure 2.3
2.4.2
Thailand ventilation chart (Khedari et al., 2000)
Field studies in Japan Japan experienced great disaster Tohoku earthquake and tsunami in 2011. It
was the most powerful earthquake ever recorded to have hit Japan, and the fourth most powerful earthquake in the world since the latest record began in 1900. To overcome the loss, they have a national movement called setsuden, which literally means ‘saving electricity’, as displayed in an example of campaign poster in Figure 2.4. This movement hopes to encourage the Japanese public to conserve electricity during the summer months, and adopt an overall energy sustainable lifestyle. Specifically, setsuden was a reaction to the aftermath of the Tohoku earthquake and tsunami, which damaged the Fukushima nuclear plant, in March 2011. The movement started in July 2011 to prevent rolling blackouts during the summer due to electricity shortages in eastern Japan (Rubin, 2011).
31
Figure 2.4
Poster of setsuden movement (Ocheltree, 2011)
As part of the movement in office buildings, there are Cool Biz campaign in summer and Warm Biz campaign in winter, which encourage people in Japan, especially office workers, to use appropriate comfortable clothings according to the seasons. During summer, blazers and ties are not compulsory in offices, while polo shirts and trainers are acceptable; even sandals and jeans are also allowed in certain circumstances. Example of clothing and adaptation during Cool Biz campaign is shown in Figure 2.5. In return of wearing comfortable clothes, air temperature settings were adjusted in order to reduce electricity use, by minimum 28.0 C for cooling mode during summer and maximum 20.0 C for heating mode during winter season (Nakashima, 2013).
(a)
(b)
Figure 2.5 Clothing adaptation during Cool biz campaign in Japan (Indraganti et al., 2013): a) using lightweight shirt and hand fan, b) clothing and hair style adjustments
32 Goto et al. (2007) separated a study of six buildings in Japan based on the occupants’ adaptive opportunity and found that when given the chance to perform adaptive behaviour, occupants’ comfort temperature tend to vary more than when such opportunities are not present. The authors also found that from a baseline of 0.5 clo, given occupants’ clothing insulation value would increase by 0.05 clo for every 5.0 C rise in outdoor temperature.
In a comparative study between American, Korean, and Japanese occupants, Kim et al. (2007) demonstrated that cultural dimensions played an important role in determining indoor temperature settings as well as occupant adaptive behaviour. The authors found that Japanese indoor temperatures were closer to the seasonal outdoor temperatures and Japanese occupants generally wear clothes more suited for the current season. This comparison between Japanese and non-Japanese occupants was also done by Nakano et al. (2002), where non-Japanese, mostly American and Australian males preferred lower temperatures of 23.5 C and 22.5 C in summer and winter respectively, while Japanese males had corresponding comfort temperatures of 24.0 to 25.0 °C. This was mostly attributed to higher occupational stress experienced by non-Japanese occupants due to the nature of their work.
A study by Tanabe et al. (2013) of occupants in five buildings in Tokyo, Japan during the summer indicated that productivity rapidly decreased as temperatures increased beyond 27.0 °C. This is counter to the recommendations in the Cool Biz scheme suggesting indoor air temperatures of 28.0 °C. This conclusion was reinforced in Rijal et al. (2016), where winter comfort temperatures of 24.3 °C and summer comfort temperatures of 25.4 °C deviate from the Japanese government recommendations of 20.0 °C and 28.0 °C respectively.
Mustapa et al. (2016) performed a comparative study of thermal comfort in CL and FR buildings in Fukuoka, Japan and found that at the same comfort temperature of 26.6 °C, estimated using the Griffiths method, occupants in FR buildings reported feeling warm while those in CL buildings reported neutral thermal sensation. The author also found that occupants in FR buildings are more active in performing adaptive behaviours to maintain thermal comfort.
33 2.5
Building Ventilation Modes
In thermal comfort studies, ventilation mode is an important variable to consider, therefore the investigated buildings in thermal comfort studies are categorised by this variable. In this study, there are three ventilation modes investigated: free running (FR), mixed mode (MM), and mechanical cooling (CL).
A free-running building is one in which no energy is being used either for heating or for cooling at the time of the survey (Nicol and Humphreys, 2010). Another study in India described free-running buildings as ‘naturally ventilated buildings with no mechanical systems for heating or cooling’ (Manu et al., 2014). The international standard CEN (2007) mentioned that most of naturally ventilated buildings are freerunning in summer, therefore there is no mechanical cooling system to dimension and the criteria for the categories are based on indoor temperature. This indoor temperature is then used to design passive thermal controls such as solar shading, thermal capacity of building, and windows’ design, orientation, and openings to prevent over heating. Based on these explanations, free-running (FR) can be defined as a mode which could be applied in any buildings (with or without HVAC system) as long as the HVAC system is in off mode. Meanwhile, natural ventilation is a means to provide air circulation, as explained in official definitions in Table 2.3.
Table 2.3 : List of definitions related to ventilation Keyword Ventilation
Natural ventilation
Mixed mode systems
Definition The process of providing air to or eliminating air from a room for the aim of adjusting air humidity levels, temperature or contaminant within the space ... ventilation established by wind, diffusion, or thermal effects through windows, doors, or other intentional openings in the built environment ... refers to a combined approach to room conditioning that uses a hybrid of mechanical systems, and natural ventilation from operable windows (either automatically or manually controlled) that include refrigeration and airdistribution equipment for cooling
Source (ASHRAE Standard 62.1, 2007) Section 3 (ASHRAE Standard 62.1, 2007) Section 3 (Center for the Built Environment (CBE) University of California Berkeley, 2013)
34 Keyword
Naturally ventilated spaces
Definition
Source
... must be constantly open to and within 8 m (25 ft) of accesible roof or wall openings to the outdoors, the openable area of which is at least 4% of the net occupiable floor area The means to operate required usable openings must be readily accessible to the users whenever the room is occupied
(ASHRAE Standard 62.1, 2007) Sections 5.1.1, 5.1.2
Naturally
The spaces where the thermal conditions of the conditioned space are controlled primarily by the building users through operating windows spaces
(ASHRAE Standard 55, 2004) Section 5.3
Mechanical Cooling of the indoor environment by mechanical means used to provide cooling of cooling supply air, fan coil units, cooled surfaces, etc
(CEN, 2007) Section 3.13
Cooling mode is what people commonly refer as ventilation mode which uses air conditioning (AC) for cooling purposes, as illustrated in Figure 2.6. However, AC is not used as a term in this study, since it might cause confusion. In four-seasons climate, AC is not only used for cooling but also heating purposes. We specifically studied the case when the AC is utilized as a cooling device in hot humid climate area.
Figure 2.6 Mechanical cooling (CL) ventilation mode (Center for the Built Environment (CBE) - University of California Berkeley, 2013) Mixed mode ventilation is defined as combination of the two mentioned ventilation modes. It claimed to have advantages over the conventional airconditioning system, such as reduced energy usage on HVAC system; higher occupant satisfaction; better health condition because of higher outdoor air ventilation rates; and enhanced flexibility as the result of distributed mechanical controls and systems (Guidelines for High Performance Buildings, 2004).
35 According to the Center for the Built Environment (CBE) - University of California Berkeley (2013), there are three types of mixed-mode ventilation, based on the time and space they are operated: concurrent, change-over, and zoned mode. Concurrent mixed-mode is indicated as ‘same space and same time’, which means both operable windows and AC system are operated simultaneously, as illustrated in Figure 2.7. In contrast, change-over mixed mode is indicated by ‘same space, different time’. This mode is increasingly popular in four-seasonal countries, since the occupants are allowed to use HVAC system during extreme temperature seasons: winter and summer, then change to only operable windows during autumn and spring.
The last one is zoned mode, indicated by ‘different space, same time’. It is also commonly used, where different conditioning strategies applied to different part of a building. For example, when mechanical cooling is only provided to meeting rooms, while other rooms use operable windows. However, operating conditions for many mixed-mode buildings sometimes deviate from their original design intent, since it will depend on the occupants behaviour. For instance, a building which is originally designed for seasonal change-over mode, may operate both AC and windows concurrently in practice.
Figure 2.7 Concurrent mixed-mode operation (Center for the Built Environment (CBE) - University of California Berkeley, 2013) In a comparative study of naturally ventilated and air-conditioned classrooms in Japan by Kwok and Chun (2003), it was found that occupants in naturally ventilated spaces, with a 3.0 °C higher average indoor temperature, consider the environment too cold, despite being within ASHRAE recommended comfort zone. Han et al. (2007) study of naturally ventilated offices in China during summer found that occupants in natural ventilated buildings are willing to accept wider temperature ranges, up to 6.3
36 °C higher than their air-conditioned counterparts. A study of British offices comparing occupants’ comfort threshold in both naturally ventilated and air-conditioned buildings also demonstrate wider comfort thresholds of 2.4 °C and 2.6 °C in summer and winter respectively (Oseland, 1998).
A study by Moujalled et al. (2008) on naturally ventilated office buildings in southern France during summer and winter showed that occupants were less sensitive to temperature changes and invalidated the application of the PMV model to this study. This finding was also found in a study of naturally ventilated Singaporean public houses by Wong et al. (2002), where occupants expressed thermal satisfaction with sufficient air velocity, through the use of fans, despite predicted thermal sensation votes of +2 to +3 using the ASHRAE scale.
In a meta-analysis comparing occupants’ subjective responses in naturally ventilated and buildings with HVAC systems, de Dear and Brager (1998) showed that occupants in the former tolerate a wider range of temperatures, which can be attributed to a combination of their behavioural and psychological adaptations.
2.6
Related Standards
There are two international standards related to this study ASHRAE Standard55 for thermal comfort and European Committee for Standardization (CEN) Standard EN15251. Both of the standards provide an equation to estimate comfort temperature based on outdoor air temperature. ASHRAE 55 comfort temperature equation is based on monthly mean outdoor air temperature (Tom) data, with upper and lower 80% acceptability limit equations as follows (ASHRAE, 2004). The recent ASHRAE Standard 55 (2013) redefined the Tom into prevailing mean outdoor air temperature (Tpma), as illustrated in Figure 2.8, but the equations remained the same as previous version, which were using Tom.
37
Figure 2.8 2013)
𝑇𝑐𝑜𝑚𝑓 = 0.31 𝑇𝑜𝑚 + 21.3
(2.12)
𝑇𝑐𝑜𝑚𝑓 = 0.31 𝑇𝑜𝑚 + 14.3
(2.13)
Acceptable Top ranges for naturally conditioned spaces (ASHRAE,
Meanwhile CEN Standards EN 15251 is based on daily running mean temperatures (𝑇𝑟𝑚 ), which could be obtained based on seven days outdoor temperature data sets prior to the calculated day. All 𝑇𝑟𝑚 in this study were obtained using Equation 2.14.
𝑇𝑟𝑚 = (𝑇𝑜𝑑−1 + 0.8𝑇𝑜𝑑−2 + 0.6𝑇𝑜𝑑−3 + 0.5𝑇𝑜𝑑−4 + 0.4𝑇𝑜𝑑−5 + 0.3𝑇𝑜𝑑−6 + 0.2𝑇𝑜𝑑−7 )/3.8
(2.14)
where 𝑇𝑜𝑑−1 is daily mean outdoor temperature for the previous day, 𝑇𝑜𝑑−2 is daily mean outdoor temperature for the day before, and so on.
Since the field measurements of this study were conducted daily, the results of FR mode were compared with Equation 2.15 from CEN Standards EN 15251 which predicts comfort temperature zones in free-running mode based on daily running mean temperature (EN 15251, 2007).
38 𝑇𝑐𝑜𝑚𝑓 = 0.33 𝑇𝑟𝑚 + 18.8
(2.15)
where 𝑇𝑐𝑜𝑚𝑓 is comfort temperature (C), and 𝑇𝑟𝑚 is daily running mean temperature (C). The upper and lower limits are calculated by adding ±2 to the equation for Category I, ±3 for Category II, and ±4 for Category III. The range of comfort operative temperature within these categories is as shown in Figure 2.9.
Comfort operative temperature (C)
33
Ⅰ:±2K Ⅱ:±3K Ⅲ:±4K
31 29 27
III II I
25 23
I II III
21 19 8
10
12
14
16
18
20
22
24
26
28
30
Outdoor running mean temperature (C)
Figure 2.9 Comfortable indoor Top for buildings without mechanical cooling systems, as a function of the outdoor running mean temperature (CEN, 2007) Currently there is no international adaptive standard to define comfort temperature zones in mechanically cooled buildings, because outdoor air infiltrations are assumed to be minimized. There is still a correlation found between outdoor and indoor air temperature in HVAC buildings, although not as significant as in freerunning modes. Therefore comfort zones for CL mode were plotted using Equation 2.16 from the Chartered Institution of Building Services Engineers (CIBSE) guidelines, specified for mechanical cooling and heating (Humphreys and Nicol, 2006).
𝑇𝑐𝑜𝑚𝑓 = 0.09 𝑇𝑟𝑚 + 22.6
(2.16)
Aside of the standard and guideline, local regulations were also added into account for each investigated location. Cool Biz campaign in Japan recommends
39 28.0 C temperature settings during summer season (Haneda, 2010). In Malaysia, recommended minimum thermostat setting in governmental office buildings is 24.0 C (Lau et al., 2009). In Indonesia, national standard SNI 6390:2011 recommends indoor air temperature to be 25.5 C (Badan Standardisasi Nasional (BSN), 2011). Meanwhile according to Singapore Standards SS 554: 2009, comfortable indoor operative temperature is between 24.0 to 26.0 C (SPRING Singapore, 2009).
2.7
Chapter Summary
This chapter reviewed the literature pertaining to thermal comfort, chronologically reintroducing the theories in thermal comfort from the heat balance equation to the adaptive thermal comfort theory used in this study. The literatures showed that human thermal comfort is affected by six main factors: air temperature, radiant temperature, air velocity, humidity, clothing insulation, and metabolism, as well as the effect of their interactions. Aside of the six mentioned factors, individual characteristics such as demographics and expectations are found to have effects on thermal comfort. Previous field studies in tropical climates produced different results, which might be caused by acclimatization. Meanwhile in Japan, field studies on thermal comfort in office buildings were conducted following the energy saving campaigns. Building ventilation modes are also reviewed in this study, which included naturally ventilated, mixed mode, and mechanically cooled systems. Finally, previous field studies and ventilation modes are linked to the current standards in use from ASHRAE, CEN, CIBSE, as well as local regulation: Indonesian Badan Standardisasi Nasional and Singapore Standards. These theories and standards formed the basis for the methodology used in this study, presented in the following chapter.
CHAPTER 3
METHODOLOGY
3.1
Introduction
This chapter provides explanations of the research design and methodology in five sections. The overall structure of methodology used in this study is presented in first section. The outdoor climate of each investigated location are discussed in the next section, followed by information of each investigated office buildings and data collection method. The last two sections explain estimation of thermal comfort variables and analytical techniques for each objective of this study.
3.2
Overall Structure of Methodology
The structure of this research consists of four phases: Preliminary Planning, Data Collection, Analysis, and Contribution. The first phase looks into literature review and research design, which includes selection of buildings and locations to investigate, as well as planning for field measurement and survey. The second phase covers data collection from the selected buildings and locations in four countries. Results from data collection are presented in two parts: objective and subjective variables. Both groups of results are then analysed in the third phase, using a number of methods such as regression, probit analysis, and Griffith’s method. As the output of analysis, the last phase presents conclusion of this study based on the objectives. The outputs of analysis are validated with current standards, such as ASHRAE Standard55 and CEN standard for FR mode, as well as CIBSE guidelines for CL mode. The
41 results are also compared with other previous field studies of office buildings in tropical climate and Japan during summer season. Diagram of overall structure of this research is provided in Figure 3.1.
Figure 3.1
3.3
Overall structure of methodology
Climate and Geographical Description
Field investigations of this research were conducted in four countries, three tropical countries in Southeast Asia: Malaysia, Indonesia, and Singapore, as well as one temperate country, Japan. To compare with the similar condition in temperate climate, field measurements in Japan were conducted during summer when the average outdoor temperature is close to the temperature in hot-humid tropical climate. The locations of investigated buildings are indicated in Figure 3.2.
42
Figure 3.2 Map of investigated locations, each country is indicated by a red circle (Cartographic Research Laboratory - The University of Alabama, 2016) Malaysia, Indonesia, and Singapore are generally hot-humid, since they share the same climate classification: tropical rainforest climate or Af, according to the Köppen–Geiger climate classification system (Kottek et al., 2006). Geographically, this climate is found within 10° latitudes on both sides of the equator, sometimes it may extend to 25° away from the equator in some eastern-coast areas. All the investigated offices in the three mentioned countries are located within this range; Malaysia at 03°08'N, Indonesia at 06°54'S, and Singapore at 01°17'N. The tropical rainforest climate actually has no natural seasons, since it is dominated by the doldrums low-pressure system throughout the year. The average precipitation were at least 2.4 inches or 60 mm and air temperature approximately above 18°C all year round (Kottek et al., 2006).
In Malaysia, the climate in Kuala Lumpur is influenced by the Titiwangsa Mountains to the east and the island of Sumatra, Indonesia to the west. Air temperature
43 is usually constant throughout the year, average monthly lows range between 23.4 – 24.6 °C and never drops below 14.4 °C, while monthly highs are around 32.0 – 33.0 °C and never exceeds 38.5 °C (WMO, 2015). In Singapore, the daily minimum and maximum air temperature hovers around 23.0 °C and 32.0 °C respectively. The highest recorded temperature is 36.0 °C, while the lowest recorded temperature was 19.4 °C (Meteorological Services Division National Environmental Agency, 2012). Meanwhile the climate in Bandung, located about 700 m above sea level, is classified as humid, and cooler than most of other Indonesian cities, due to its elevation. The annual average temperature is 23.6 °C (Bureau of Statistics, 2003).
Japan is classified as temperate zone and has four distinct seasons. The climate varies from warm temperate climates in the south to cool temperate in the north. Investigated office buildings in this study are located in Yokohama and Tokyo, neighboring cities in the Kanto region. The climate is warm temperate, or Cfa, based on Köppen–Geiger climate classification system, which usually occurs on the eastern sides and coasts of continents, about 30o – 50o latitude (Kottek et al., 2006). Average temperature in Kanto during summer season is 25.4°C with 154 mm average precipitation. During second half of summer season in Japan, it experiences hot, humid, and sunny weather; since the North Pacific high moves toward northwest part of the country. Sometimes there is also Yamase, easterly winds caused by the Okhotsk high, resulting in cloudy and rainy weather in Kanto during summer season (Japan Meteorological Agency, 2012). Therefore, despite of different climate classifications, summer season in Japan has similar condition with tropical climate, which is hot and humid.
The annual variation in the outdoor air temperature for each study location is indicated in Figure 3.3. The graph shows that the outdoor air temperatures remained relatively constant throughout the year in Malaysia, Singapore, and Indonesia. The monthly mean temperature in Malaysia and Singapore was identical, while it was approximately 3 °C lower in Indonesia. The cooler air temperature found for Indonesia is ascribed to the effect of the higher elevation (approximately 700 metre above sea level) of Bandung city, where the survey was conducted. The monthly mean temperature in Japan varies significantly along with the seasonal cycle; therefore, the
44 field surveys in Tokyo and Yokohama were conducted specifically during the summer season. Despite the climatic differences, the monthly mean outdoor air temperature in Japan at the time of the field study was quite similar to that of Indonesia, with a deviation of only 1.4 °C.
Figure 3.3 Annual variation of monthly statistics of outdoor air temperature of year 2015 and measurement timeline for each country In Figure 3.3, MY refers to the data obtained from the weather data station located at the study building, Malaysia-Japan International Institute of Technology (MJIIT) building at the Universiti Teknologi Malaysia (UTM), Kuala Lumpur campus, Malaysia. ID refers to the data observed from the weather data station in Husein Sastranegara Airport, Indonesia, located approximately 4.5 km from the assessed building (the target of the measurement). SG refers to the data observed from the weather data station at Changi Airport, Singapore, approximately 3 km from the assessed building. JP refers to the data observed from the Tokyo observation point, within the Automated Meteorological Data Acquisition System (AMeDAS) network (Japan Meteorological Agency, 2015), which is approximately 16 km from the assessed building.
45 3.4
Investigated Office Buildings
In this study, we attempted to perform comparisons between buildings with similar characteristics across different ventilation modes in various countries in hot and humid season. The investigated offices are required to be open-plan, where several workers occupied a relatively large space together, sometimes separated with low cubicle partitions. This design allows the research instruments to be installed efficiently in each working area. The study also required office workers to provide their personal information and participate multiple times in questionnaire survey. Obtaining permission to perform thermal comfort studies in buildings that meet these criteria is challenging; therefore, this study broadens the office workers’ demographic, including age range, to include a broader cross-section of respondents. The available data were also depending on occupants’ willingness to provide them. In Singapore, some information was not available, i.e. occupants’ weight and height, as well as their perception on humidity. In Japan, there were additional information provided by the respondents, i.e. their preference on using HVAC for heating and cooling, as will be explained in the next chapter. This information is useful since this study focused on their thermal comfort during summer season only, whereas the people living in Japan have to adapt to the seasonal changes.
Altogether, there are 325 respondents surveyed and 2049 field measurement data collected from the four countries. The data was obtained from two investigated buildings in Singapore: S1 and S2; three buildings in Indonesia: I1, I2, and I3; four buildings in Malaysia: M1, M2-a, M2-b, and M2-c; also four buildings in Japan: J1-a, J1-b, J1-c, and J2. The buildings in Shah Alam: M2-a, M2-b, and M2-c are located in one campus area; same condition applies to J1-a, J1-b, and J1-c, which is located in Yokohama campus. All the buildings listed in Table 3.1 are office spaces in university campuses, except for S1 and S2 in Singapore and I1 in Indonesia; which are located in mixed-use commercial area. The details for each building are explained in the next subsections.
Table 3.1 : Investigated offices in each country Country
City
Location
Survey period
Singapore
Singapore
118’N, 10350’E
8/1/2015 – 9/1/2015 (8 days)
Indonesia
Bandung
6°53'S, 107°36'E
24/2/2015 –12/3/2015 (13 days) 13/4/2015 – 5/5/2015 (20 days)
Kuala Lumpur
308’N, 10142’E
Shah Alam (Selangor)
3°04'N, 101°30'E
5/3/2015 – 21/5/2015 (29 days)
Yokohama
35°33'N, 139°34'E
1/9/2015 – 25/9/2015 (4 days)
Setagaya (Tokyo)
35°35'N 139°39'E
Building code S1 S2 I1 I2 I3 M1
Malaysia
Japan
n total
M2-a M2-b M2-c J1-a J1-b J1-c
14/9/2015 – 18/9/2015 J2 (3 days)
Mode
FL/TF
13/20 4/4 CL 8/13 FR 2/5 MM 3/3 4/10 8/10 CL 8/10 10/10 1/2 CL 1/1 1/1 1/2 CL 2/2 and FR 5/5 1/4 CL and FR 4/4 CL
Orientation S-E N-E N-W S-N S-N E N-E S-E E W S-W S-W S N S-N W-E W
n people
n data
14
56
16 18 20 15 15 39 21
91 159 150
40
486
40
173
87
282
325
2049
652
Note: CL: Cooling, MM: Mixed Mode, FR: Free-running; FL/TF: Floor Level/Total Floors of the building; Orientation: S: South, E: East, N: North, W: West, n people: Number of respondents, n data: Number of measurement sample data.
46
47
3.4.1
Singapore There are two investigated offices in Singapore, S1 in central region and S2 in
east region of Singapore; with total floor area for each 359.1 m2 and 204.0 m2 respectively. Field survey and measurement for indoor climatic variables were conducted from January 8th until January 29th 2015. Field survey was only performed once in each office, whereas field measurement for indoor climatic variables was executed four times each with one-week interval between each measurement. The details for each office are as shown in Table 3.2.
Table 3.2 : Detail information for each investigated offices in Singapore Description Type of ventilation Location Building Total Storey Building Orientation Respondents Measurement date Duration Façade
S1 Centralized AC central region of Singapore 19 storeys with 1 basement carpark
Buildings S2 Split unit AC east region of Singapore 4 storeys high, no basement carpark
South-East
North-East
8 (on 08/01/2015) 8th, 14th, 21st and 28th January 2015 4 days
6 (on 09/01/2015) 9th, 15th, 22nd and 29th January 2015 4 days Curtain Walling system with aluminium panels
Curtain Walling System
Both of the buildings are using mechanical cooling (CL) ventilation mode. The centralized AC thermostat setting in office S1 was between 22.0 and 23.0 °C from 19th January onwards, while AC system in office S2 was a direct expansion AC (DX) split unit and four-way ceiling cassette system, therefore the thermostat settings could be
48 changed individually. As seen in Figure 3.4, both buildings use curtain wall system on their façade, a typical design for fully air-conditioned office buildings.
(a) Figure 3.4
(b) Investigated building's facade in Singapore a) S1, b) S2
Instruments for measuring and recording each office indoor climatic parameters were set in several places within both offices. In office S1, there are three rooms where the instruments are being set: small meeting room, big meeting room, and working area; as seen in Figure 3.5a. One set of instruments was assigned for each of the meeting room. In working area, the instruments were located in three points, in front, middle, and the end of working area. The distribution of measuring instruments is made as explained above, considering the respondents mostly stay in the working area rather than the other two rooms. Meanwhile in office S2, there are five spots where the instruments are set: reception, meeting room, working area, individual offices, and printing area, as shown in Figure 3.5b. Just like in office S1, most respondents in S2 were occupying working area. In this study, thermal comfort analysis was conducted only on data taken from working areas. The measurements taken from other areas (e.g. meeting rooms & reception areas) will only be used for reference purposes.
49
Legend Occupants Instruments
(a)
Legend Occupants Instruments
(b) Figure 3.5
Seating and instruments layout in Singapore a) Office S1, b) Office S2
Both investigated offices have open-plan working space with low cubicles to separate between occupant’s desks and air conditioning outlets are installed on ceilings as shown in Figure 3.6.
50
(a) Figure 3.6
3.4.2
(b) Working area in a) Office S1, b) Office S2
Indonesia Another field study was completed in Bandung, a city in West Java province,
Indonesia. There are three investigated offices: I1, I2, and I3; with floor area 95.5 m2, 118.3 m2, and 108.0 m2 respectively. Office I1 is located in a high-rise commercial building with CL ventilation mode in downtown area of Bandung; while the other two investigated buildings in Indonesia are located in Institut Teknologi Bandung campus area, I2 was using free-running (FR) and I3 was using mixed-mode (MM) ventilation. The detailed information of each investigated offices in Indonesia is displayed in Table 3.3.
51
Table 3.3 : Detail information for each investigated offices in Indonesia Description
I1
Type of ventilation Location Windows Orientation Building Total Storey Occupants Measurement date Duration Number of Responses Façade
Buildings I2
Centralized AC
Free-running
I3 Mixed Mode: Concurrent
8th Floor of a mixed-use building in central area of Bandung North-West direction 12 storeys high with 1 basement carpark 16 10/03/2015 – 12/03/2015 3 days
2nd Floor of Labtek IXA Building, ITB Campus
3rd Floor of West Campus Center Building, ITB Campus
South-North direction
18 02/03/2015 – 06/03/2015 5 days
South-North direction 2 storeys with 1 semi-basement public area 20 24/02/2015 – 02/03/2015 5 days
96
150
150
Curtain Walling system and aluminum panels
Bricks and concrete, with overhang
Curtain walling system and concrete, with overhang
4 storeys, no basement
The ventilation modes are quite relevant with the design of each building façade. As seen in Figure 3.7a, the façade of office I1 is quite similar with the style of investigated offices in Singapore, which also uses a curtain wall system. It is a typical façade style for offices equipped with full-time mechanical cooling ventilation. Meanwhile office I2 and I3 are equipped with overhang roofs, which is quite useful for buildings with operable windows in tropical climate, since it will provide shading from solar radiation as well as rain.
(a)
(b)
52
(c) Figure 3.7 c) Office I3
Investigated buildings facade in Indonesia a) Office I1, b) Office I2,
Field measurement and survey was executed first in office I3, followed by field investigation in office I2 five days later. Both of the buildings where office I3 and I2 are located have south-north orientation for their windows. Office I3 is on the south wing of the building, with full glass curtain-walling system and operable windows on south and west side, as shown in Figure 3.8. Mixed mode ventilation is defined since occupants in I3 simultaneously opened windows while using air conditioning during field measurement. Initially this could be seen as adaptive behaviour in an airconditioned building, if it only occurs occasionally. However, because it happens constantly, it is also possible to categorize this building as using ‘concurrent mixed mode’ ventilation.
According to Center for the Built Environment (CBE) definitions, concurrent mixed-mode is a condition where operable windows and air-conditioning system are utilized simultaneously in the same space (CBE - University of California Berkeley, 2013). Because I3 was recorded to have opened windows all the time during field measurement, it is assumed that the occupants use natural ventilation simultaneously with mechanical cooling. As mentioned by Brager et al. (2007), concurrent systems have the potential to blow conditioned air straight out of the window, which might be resulting in poor performance. One of the strategy to overcome this was using high exhaust vents to minimize conflict from concurrent operation. The air-conditioning outlets in Office I3 were located on the ceiling, which was at 3.6 metres height from floor level.
53
Figure 3.8 Seating and instruments layout in Office I3 (Numerals refer to occupants’ seating position number) Office I2 is located on the 2nd floor of a five storey building, with traditional sloped roof and overhang roofs attached over each floor level, as shown in Figure 3.7b. The office is located on north wing of the building, so the windows are facing north side, except for a locket window to provide administration services, which is facing to the aisle on south side. All the windows were always opened during this field measurement, indicating the use of free-running mode in office I2. Meanwhile office I1 is located on the 8th floor of a 13 storey commercial office building in downtown Bandung. The building entrance faces north while the building façade is covered with curtain wall system in all four cardinal directions. Office I1 is equipped with fixed, non-operable windows on its west and south walls, which is also covered with fabric blinds. The air conditioning outlets are located on the ceiling and it has centralized thermostat setting system.
3.4.3
Malaysia Field measurement in Malaysia was arranged in two cities, Kuala Lumpur and
Shah Alam, Selangor. In Kuala Lumpur, it was performed at the MJIIT building in UTM campus in four postgraduate working spaces; while in Shah Alam it was conducted in three administrative office buildings inside of Universiti Teknologi Mara
54 (UiTM) campus area. MJIIT building is a ten-storey medium-rise building with a brick and concrete façade as shown in Figure 3.9.
Figure 3.9
Investigated building’s façade in Kuala Lumpur, Malaysia
Postgraduate laboratory in MJIIT refers to workspaces for postgraduate students, which is sometimes also occupied by staff (i.e. research assistants), on various floors and with differing orientations within the MJIIT building, as shown in Table 3.4. Table 3.4 : Detail information for each investigated working spaces in Kuala Lumpur, Malaysia Description Type of ventilation Location in Building Windows Orientation Respondents Measurement date Duration Number of Responses Façade
Lab 1 Split unit AC
Postgraduate Lab Lab 2 Lab 3 Split unit Split unit AC AC
Lab 4 Split unit AC
Level 10
Level 8
Level 4
Level 8
A&B:West, C: East A: 15, B: 5, C: 3 Total: 23 13 – 17/4/2015 5 days
North
East
South-East
16
16
51
20 – 24/4/2015 5 days
20 – 24/4/2015 5 days
27/4/2015 – 4/5/2015 5 days
164
120
120
264
Brick and concrete
The first measurement in the building was carried out on April 13th-17th 2015 in Lab 1, which consists of three rooms: rooms A, B, and C; the floor area for each room is 59.4 m2, 18.2 m2, and 16.1 m2 respectively. Room A and B are connected with a tempered glass door, while room C is separated by a corridor from the other two rooms. There are fifteen occupants in room A, five occupants in room B, and three
55 occupants in room C. As seen in Figure 3.10, instruments are set up in four locations in room A and in the centre of the working place in room B.
(a)
(b) Figure 3.10
Seating and instruments layout in Lab 1 a) Room A and B, b) Room C
Room A is surrounded by tempered glass walls in all direction, except for a gypsum board wall on the north side and an outside facing brick wall on the east side. About 50% of the wall consists of windows covered with blinds. Room B is located next to a corridor and has no access to outdoor air or lighting. Air conditioning outlets in room A and B are installed on ceilings. Room C has windows facing west, and the air conditioning outlet is located on the north side of the wall as shown in Figure 3.11.
56
(a) Figure 3.11
(b)
(c)
Photo of working area in Lab 1 a) Room A, b) Room B, c) Room C
The next field measurements were carried out in both Lab 2 and Lab 3 simultaneously on April 20th – 24th 2015. Both laboratories are each occupied by 16 students and staffs; while floor area is 90.0 m2 and 52.3 m2 for Lab 2 and Lab 3 respectively. The occupants’ seating position and instruments layout in both laboratories are illustrated in Figure 3.12.
(a)
(b) Figure 3.12
Seating and instruments layout in a) Lab 2, b) Lab 3
In Lab 2, field measurement was done in the working area, which has a north orientation and is neighboured by individual offices on its east side, as shown in Figure 3.13a. There are two windows covered with fabric blinds on the north wall, but the area nearby the windows are unoccupied; therefore, the working areas are hardly
57 affected by outdoor interference, as shown in Figure 3.13b. Meanwhile in Lab 3, the occupied space is almost half the size of working area of Lab 2, and the windows on east side are adjacent to working space as shown in Figure 3.13c.
(a)
(b)
(c)
Figure 3.13 Photo of postgraduate labs a) Working area in Lab 2, b) Unoccupied space in Lab 2 nearby windows, c) Working space in Lab 3 Another office space that was investigated is the Lab 4, which has a working area of 185.2 m2. This lab is located on the same floor as Lab 2, but has different orientation. The outside facing windows are located on south and east sides of the indoor working space, with fabric blinds covering each window, taking up around 60% of the wall area on the said direction as shown in Figure 3.14. There are also windows facing indoor corridors on west and north directions. During measurements, all these windows were always closed. Lab 4 is occupied by 51 people, consisting of students and staffs. Their seating layout in the lab is as seen in Figure 3.15, as well as the instruments layout in between occupants’ working space.
(a) Figure 3.14 work place
(b)
Photo of working area in Lab 4 a) south and east walls, b) north area of
58
Figure 3.15
Seating and instruments layout in Lab 4
In UiTM Shah Alam, the field measurements were organized at three different low-rise buildings (either one or two storeys) within the campus area, as shown in Table 3.5. Due to different working shifts, field survey duration varied between the offices. It was started in office M2-a, which is located on second floor of two storey building. The working area is isolated from outside air, since there is no outside-facing window. The next investigated building, M2-b and M2-c are both single storey buildings with no operable windows as shown in Figure 3.16.
Table 3.5 : Detail information for investigated offices in UiTM Shah Alam, Malaysia M2-a
Buildings M2-b
Type of ventilation
Split unit AC
Split unit AC
Split unit AC
Location
Pejabat fasiliti
Bendahari
Zon Kewangan
2
Single storey
Single storey
20 07-21/05/2015 10am – 3pm 10 days
14 05-25/03/2015 10am – 3pm 9 days
10 06-24/04/2015 10am – 3pm 10 days
64
99
62
Description
Building Total Storey Occupants Measurement date Duration Number of Responses
M2-c
59
(a)
(b)
Figure 3.16 Photo of working area in UiTM Shah Alam, Malaysia a) Office M2-a, b) Office M2-b
3.4.4
Japan In Japan, field studies were executed in two campuses of Tokyo City
University (TCU): J1-a, J1-b, and J1-c in Yokohama campus and J2 in Setagaya campus. The investigated offices in Yokohama campus were located in three separate buildings within campus area, while in Setagaya it is located in two different floor levels in one building. The building façade in Japan are made of concrete with some operable and fixed windows. The three offices in Yokohama campus were using tiles as finishing materials on the building facade, while in office J2 there are overhang roofs over the operable windows as shown in Figure 3.17.
(a) Figure 3.17
(b)
Investigated buildings’ facade in Japan a) Office J1, b) Office J2
There are three investigated buildings in Yokohama Campus, namely J1-a, J1b, and J1-c. J1-a is an administration office building in the campus area, facing east, where field investigation was conducted on levels 1 and 2 of the two-storey building. The windows in each office space in J1-a face north. J1-b is located across J1-a, the entrance face west and the windows south-facing. Investigations were also conducted
60 in another administration office and librarian working space in J1-b, both located on first floor of the two-storey building. Meanwhile J1-c is a building mostly consisted of classrooms and laboratories and the investigation was conducted in an administrative office on 5th floor of the building, with windows facing north direction. In Setagaya Campus, Tokyo, field measurement was accomplished in building J2, which is a fourstorey building with west-east orientation. The investigated office spaces are located on 1st floor and 4th floor. Summary of information in each office is as shown in Table 3.6.
Table 3.6 : Detail information for investigated offices in Japan Description Type of ventilation Location Building Total Storey Windows Orientation Respondents Measurement date Number of responses
Buildings J1 J2 Mixed mode change-over: Mixed mode change-over: FR and CL at separated FR and CL at separated events events TCU Setagaya Campus, TCU Yokohama Campus Tokyo J1-a: 2, J1-b: 2, J1-c: 5
4 storeys
North-south
West-east
40 1st, 2nd, 15th, 25th September 2015 (4 days)
96 14th, 16th, 18th September 2015 (3 days)
173
282
All the buildings were using the same ventilation design. Each office space has air-conditioning (AC) outlet on ceiling which can be operated individually and operable windows to use when the weather is considered comfortable to allow working without using AC, as shown in photo of office spaces in Figure 3.18. This could be referred as change-over mixed-mode ventilation; which contrasts with concurrent mixed mode in Indonesia, because FR and CL mode happen at different time and space. Since ventilation concept during measurement was similar with ordinary FR and CL mode, they are categorized into the same group and not considered as MM.
61
(a) Figure 3.18
3.5
(b)
Photo of working area in Japan a) Office J1, b) Office J2
Data Collection Method
Field study of thermal comfort is a combination of quantitative and qualitative data collection. The indoor climatic parameters were collected through field measurements, while other personal or subjective parameters were obtained from questionnaire survey. Both data collection methods were performed simultaneously, in order to investigate human perceptions of their thermal surroundings. In each location, the data was collected in two sessions per day: morning session between 10.00 am to 12.00 pm and afternoon session between 2.00 pm to 4.00 pm.
3.5.1
Field Measurement Three of the indoor climatic parameters affect thermal comfort: air
temperature, globe temperature, and relative humidity were measured and recorded concurrently using thermo recorders. The thermo recorder is equipped with time-series data logger with 0.05 C resolutions to record air temperature and relative humidity. It comes along with two external channels, each was attached to external air temperature sensor cable tmc1-hd, with additional 40 millimetres black-coloured sphere at the tip of one of the sensors to measure globe temperature, as shown in Figure 3.19a. The data logger was programmed to record data at 10 second intervals for at least 10 minutes until the respondents finished filling out questionnaires. Concurrently, air velocity was recorded using a hot wire anemometer with 10 seconds interval data logger, connected to an omnidirectional probe type sensor, as shown in Figure 3.19b. Additionally, surface temperatures from four cardinal directions plus ceiling and floor were
62 measured using an infrared thermometer, as shown in Figure 3.19c. The surface temperature was measured once for each session.
(a)
(b)
(c)
Figure 3.19 Instruments for indoor field measurements a) Thermo recorder with external sensor and black 40mm sphere, b) Hot-wire anemometer with probe sensor, c) Infrared thermometer for surface temperature The occupants’ seating layout, building orientation, facade, and windows’ position were also documented, as presented in Section 3.4. The details of each instrument are as seen in Table 3.7.
Table 3.7 : Instrument Specifications for Field Measurement Instrument
Parameters Measured
Type of Sensor
Relative Humidity Internal Sensor HOBO Thermo Recorder U12-13
Air Temperature Globe Temperature
External Sensor Cable tmc1-hd External Sensor Cable tmc1-hd + 40 mm Black Sphere
Measurement Resolution
Accuracy
0.03% RH
±2.5% RH [10% to 90%]
o
0.05 C
Kanomax Hot-Wire Anemometer 6501
Air Velocity
Needle Probe 6542-2G
0.01 m/s
IR-300 Infrared Thermometer
Surface Temperature
Infra-red 1 cm radius
0.1 o C
±0.25o C [0o to 50o C] ± (2% of reading ± 0.0125) m/s [0.10 to 30.0 m/s] ±0.3o C [−55o to 220o C]
63 The indoor thermal measurement instruments (i.e. thermo recorders and hotwire anemometer) were each mounted to a clamp on laboratory retort stand, as shown in Figure 3.20, and placed in the center of the investigated room. The rest of retort stands which only hold the thermo recorders were placed at approximately 1 meter away from the respondents. The height of each instrument’s sensors was adjusted to a height of 1.2 meters above floor level; the approximate height of human’s head in seated position.
Figure 3.20
The instruments set up on a retort stand with adjustable height
The data used for analysis were the average values of at least 10 minutes recording time for each point of measurement where the retort stand was located, which was in various location within the office rooms, as illustrated in Section 3.4. Because of the heat radiation from the windows, sometimes the average value of climatic parameters experienced by occupants in one point could be different from another, despite being in the same room. By setting up the instruments in different points within the investigated office rooms, these differences could be captured.
Before conducting field measurements, all temperature data logger instruments were tested with Graphtec midi logger to make sure there would be no error. The instrument is shown in Figure 3.21, attached with thermocouple type K, which has high accuracy (0.05 % of reading + 1.0 °C). The results of testing process showed that all instruments are at least 95% accurate, with R2 > 0.95, as provided in Appendix A.
64
Figure 3.21
Graphtec midi logger type GL820
At the same time of field measurement for indoor climatic parameters, outdoor temperatures were measured and recorded. In Singapore, Indonesia, and Shah Alam (Malaysia), outdoor air temperature was measured using the same instrument used for indoor measurement: thermo recorders at 10 seconds interval for the duration of field survey. The sensor was located outside of each investigated building, attached under solar radiation shields to prevent exposure to direct sunlight as shown in Figure 3.22. In Kuala Lumpur, outdoor air temperature data was collected through a weather data station located on the rooftop of MJIIT building. In Japan, outdoor air temperature data was obtained from nearest meteorological station in each city, Tokyo and Yokohama, within the AMeDAS network.
Figure 3.22 Instrument for recording outdoor temperature, covered with solar radiation shield
3.5.2
Questionnaire Survey During the physical thermal parameters measurement, questionnaires were
distributed to the occupants in the measured rooms, once in each session. The information investigated through this questionnaire are socio-demographic (i.e. nationality, gender, age, height, weight); occupants’ sensation, preference, and
65 acceptability on temperature, humidity, and air velocity; overall comfort, adaptive action to overcome thermal discomfort, and clothing items worn at the time of investigation. In Japan, socio-demographic or personal information was extended to include exercise hours per day, occupation, hometown, as well as sensitivity to cold and hot environment. The results of this extended survey can be found in Appendix C.
This study was using transverse and semi-longitudinal method. Using adaptive thermal comfort approach, there was no control over the study indoor environment, therefore the semi-longitudinal method was intended to cover different condition during working hours. The questionnaires were answered repetitively to find out consistency of the measured parameters. Respondents were each asked to answer the questionnaire between six to ten times, divided into two sessions in each measurement day, morning and afternoon. They were also asked about their health condition, then responses from occupants who voted for unhealthy condition were eliminated to avoid bias. In Kuala Lumpur and Singapore, this questionnaire was distributed in English, while in other locations it was translated to Indonesian, Malay, and Japanese according to local respondents; copies of each translation are attached in Appendix B.
As seen in Table 3.8, Thermal Sensation Vote (TSV) in this study uses the ASHRAE seven-point scale (ASHRAE, 2010). In Japan, the questionnaire used the Society of Heating, Air-conditioning, and Sanitary Engineering of Japan (SHASE) seven-point scale, because the terms “cool” and “warm” that is used in the ASHRAE scale has positive connotations in Japanese translation, which may cause it to be misinterpreted as a comfortable condition (Rijal et al., 2015).
The rest of the scales used, as shown in Table 3.8 are part of attempt to quantify qualitative data, which are actually subjective parameters based on the respondents’ answer on questionnaire. Besides the questionnaire, there was also direct observation of occupant activities such as operating windows, fans, or air conditioner; drinking water; and changing clothes.
66 Table 3.8 : Scale for thermal comfort questionnaire
Scale −3 −2 −1 0 1 2 3
Thermal and Humidity Sensation TSV (ASHRAE) TSV (SHASE) Cold Very cold Cool Cold Slightly cool Slightly cold Neutral (Neither cool nor Neutral (Neither cold warm) nor hot) Slightly warm Slightly hot Warm Hot Hot Very hot
HF Very dry Dry Slightly dry Neither humid nor dry Slightly humid Humid Very humid
Preference Scale −2 −1 0 1 2
TP (Nicol) Much cooler A bit cooler No change A bit warmer Much warmer
HP Dry considerably Slightly dry No change Slightly humid Humid considerably
Comfort, Health, and Air Movement Scale 1 2 3 4 5 6
Overall comfort Very uncomfortable Uncomfortable Slightly uncomfortable Slightly comfortable Comfortable Very comfortable
Health condition Bad Not good Fair Good
AV No air movement Weak Moderate Strong
Note: TSV: Thermal Sensation Vote, ASHRAE: American Society of Heating, Air conditioning and Refrigeration Engineers, SHASE: The Society of Heating, Air conditioning, and Sanitary Engineering of Japan, HF: Humidity Feeling, TP: Thermal Preference, HP: Humidity Preference, AV: Air movement vote.
3.6
Estimation of Thermal Comfort Variables
Some variables cannot be obtained directly through field measurements or questionnaire surveys, thus need to be estimated based on primary data. Those variables are mean radiant temperature, operative temperature, absolute humidity, body surface area, predicted mean vote (PMV), and predicted percentage of dissatisfied (PPD).
67 The equations to calculate PMV and PPD has been explained in section 2.2.2, and this study uses a programming tool as recommended in ISO 7730 (2010), which was developed from the previously explained equations. This programming tool was embedded as a macro module in MS Excel. Other than PMV and PPD, the four estimated variables are explained in following subsections.
3.6.1
Thermal indices There are four thermal indices used as variables in this study, namely air
temperature (Ta), globe temperature (Tg), mean radiant temperature (Tmrt), and operative temperature (Top). The former two are measured directly using data logger, while the latter two are obtained through calculation based on the measured parameters. This study uses globe thermometer method and calculates Tmrt using Equation 3.1 (ASHRAE, 2005). Other than this method, there is also the integral radiation measurement method to obtain mean radiant temperature. However, a recent study revealed that there is only relatively small differences between both methods, even in an outdoor setting (Thorsson et al., 2007).
1
𝑇𝑚𝑟𝑡
4 1.1 × 108 𝑉𝑎0.6 = [(𝑇𝑔 + 273) + − 𝑇 − 273 (𝑇 )] 𝑔 𝑎 𝜀𝐷 0.4 4
(3.1)
where 𝜀 refers to emissivity of the globe, taken as 0.95 for a black globe, and D is the diameter of the globe which is fixed at 0.04 m.
Meanwhile, Top is the weighted average value of air temperature and mean radiant temperature to express their joint effect. The weighting factors are radiative and convective heat transfer coefficients at the occupant’s clothed surface. Top is approximated with Equation 3.2 (International Standard Organization, 1998).
68
𝑇𝑜𝑝 =
(𝑇𝑚𝑟𝑡 + 𝑇𝑎 × √10𝑉𝑎 ) 1 + √10𝑉𝑎
(3.2)
At indoor condition when average of air speeds is less than 0.20 m/s, Top is approximated with Equation 3.3 (ASHRAE, 2010).
𝑇𝑜𝑝 = 0.5 (𝑇𝑎 + 𝑇𝑚𝑟𝑡 )
3.6.2
(3.3)
Absolute Humidity In field measurements, humidity information is only obtained from relative
humidity (RH), which is measured directly and simultaneously with indoor air temperature, using digital thermo recorders. It is important to note that the same value of RH for different temperatures does not represent the same amount of real humidity, or water vapour contained in the air. Because of this, absolute humidity data is necessary for further analysis on the effects of humidity on thermal comfort.
Absolute humidity data in this study is calculated based on Ta and RH, by estimating water vapour pressure in the process. It is measured as the ratio of water vapour for each kilogram of dry air, using Equation 3.4 (Reisel, 2016).
𝐴𝐻 =
𝑘𝑔 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟 𝑣𝑎𝑝𝑜𝑢𝑟 𝑝𝑣 ⁄𝑅𝑣 = 𝑘𝑔 𝑜𝑓 𝑑𝑟𝑦 𝑎𝑖𝑟 (𝑝𝑡 − 𝑝𝑣 )⁄𝑅𝑎
(3.4)
where AH is absolute humidity, which represents humidity ratio in kgv/kgda. pv is partial pressure of water vapour, while pt is total barometric pressure. Rv is gas constant of water vapour and Ra is gas constant of air; both variables could be substituted to make Equation 3.5.
𝐴𝐻 = 0.622
𝑝𝑣 𝑝𝑡 − 𝑝𝑣
(3.5)
69 The partial pressure of water vapour for any temperature above the freezing point of water T, is given by the Goff-Gratch Equation 3.6.
373.16 373.16 log10 𝑒𝑊 = −7.90298 ( ) + 5.02808 log10 ( ) − 1.3816 𝑇 𝑇 𝑇
× 10−7 (1011.344(373.16) − 1) + 8.1328 × 10−3 (10−3.49149(
373.16 ) 𝑇
(3.6)
− 1) + log10 (1013.246)
where T is the air temperature in K and 𝑒𝑊 is the partial pressure of water vapour in hPA. At temperature T, the gas constant of water vapour is given by Equation 3.7.
𝑝𝑣 = 𝑅𝐻 ×
760 × 𝑒𝑊 1.033
(3.7)
where RH is the relative humidity in decimals. With the total barometric pressure as constant; 𝑝𝑡 = 760 mmHg, this can then be used to calculate absolute humidity using Equation 3.5.
3.6.3
Body Surface Area Since the information of respondents’ weight and height has been collected
through questionnaire, this study uses Equation 3.8 to calculate Body Surface Area (AD) (DuBois and DuBois 1916; Cornell University School of Medicine 2000) ,
AD = 0.007184 × w0.425 × h0.725
(3.8)
where AD refers to Dubois body surface area (m2), w refers to weight of body (kg), and h refers to height of body (m).
There are a few other methods to calculate body surface area, however, AD has been traditionally used in heat balance equation (Parsons, 2003). The nature of heat
70 balance equations also means that any error in this variable is insignificant. In other cases, sometimes 1.8 m2 is used as a standard body surface area value for a man with a height of 1.73 m and a weight of 70 kg.
3.7
Analytical Techniques
All the data collected in a series of field measurements were analysed using several analytical methods. These methods are mostly quantitative, since the qualitative parts are quantified through some voting scales, as explained in previous section. Quantitative analyses are performed with Microsoft Excel 2010 and IBM SPSS Statistics version 22. Aside of the basic descriptive statistical analysis, there are three analytical methods used in this study, mainly to investigate comfort temperature: regression method, probit analysis method, and Griffiths’ method.
3.7.1
Regression method Regression method is used in this study to predict on which temperature level
most people would vote for neutral or comfortable, based on real data from field measurements. A linear regression analysis is performed using IBM SPSS Statistics, using TSV as the dependent variable and one of thermal indices (Ta, Tg, Tmrt, or Top) as the independent variable. Scatterplot graph is also plotted as a visual aid to show trend lines, which are projection of equation lines. The downside of this method is sometimes regression results might not be significant, or have p-values of more than 0.001 (for confidence interval 95%); which often happens on dataset with small sample, although the number of sample is not always a determinant of significance.
As a comparison, PMV is also plotted as the dependent variable against one of thermal indices as the independent variable. The variables should not be swapped to one another, since TSV and PMV are subjective variables which depend on the temperatures, and not the other way around in actual conditions. Besides analysing comfort temperature range, regression method also used to analyse the factors affecting thermal comfort, in lieu of the obvious correlation method. For example,
71 humidity vote as dependent variable is regressed with objective parameters such as relative humidity.
3.7.2
Probit analysis method To predict thermal comfort zone for the population, TSV results from
questionnaire survey are analysed using probit regression method (Finney, 1971). Ordinal regression is performed with probit as the link function and measured temperature as covariate. Significance values are provided from the ordinal regression, and those with p-values of less than 0.05 further analysed with this method. Mean temperatures were calculated by dividing the constant by regression coefficient, and then each P was calculated by using Equation 3.9. Probability = CDF.NORMAL (quant, mean, S.D.)
(3.9)
where CDF.NORMAL is Cumulative Distribution Function for the normal distribution, “quant” is operative temperature (C), and S.D. is standard deviation. Afterwards, each P function was plotted into proportions which illustrate comfort vote area for each temperature condition. As the temperature rises, the proportion of people voted for cool sensation is expected to diminish while proportion of warm sensation votes increase. To see proportion of comfort, the P which represents comfortable vote (TSV −1, 0, or 1) were subtracted to form a bell-shaped curve. From the graph, we could estimate the optimum temperature when most people would claim to be comfortable.
3.7.3
Griffiths’ method This analytical method was first applied by Griffiths (1990), when he
conducted field measurement on small sample numbers. Since the data is insufficient to provide a reliable regression analysis, Griffiths estimated that for each comfort vote, such as on the seven-scale TSV, temperature rises would be 3 K, based on numerous climate chamber experiments in the past. Through this method, comfort temperature could be calculated even from a single vote, although it works with assumption that
72 there is no adaptation. Griffiths’ method provides Equation 3.10 to calculate comfort temperature based on TSV as follows, (Griffiths, 1990),
𝑇𝑐 = 𝑇 +
(0 − 𝑇𝑆𝑉) 𝑎
(3.10)
Here Tc indicates comfort temperature (C), based on T which is indoor temperature (C). TSV is thermal sensation vote on a scale where 0 is neutral condition. In other cases, 0 could be replaced with other number which represents neutral condition in the study. 𝑎 represents the constant rate of thermal sensation change with room temperature, which is actually equal to the regression coefficient. In applying this equation, 0.25, 0.33, and 0.50 were used as a, the Griffiths’ constant, by Nicol et al. (1994), Rijal et al. (2010), and Mustapa et al. (2016). However, Nicol and Humphreys (2010) argued that the actual value of the constant must be greater than 0.4, based on analysis on standard deviations of Top for each regression coefficient in 4655 sets of data in The EU Project Smart Controls and Thermal Comfort (SCATs).
3.8
Chapter Summary
The methodology used in this research was mainly field investigation, measuring both objective and subjective variables. The overall structure diagram summarized the whole research as four systematic phases: the preliminary planning, data collection, analysis, and contribution phase. The chapter started with detail explanantions of selected buildings and locations, beginning with general information such as climate of each country, then zoomed into target buildings in each country, and further described each investigated office located in these buildings. The information provided was including building orientation, façade materials, floor levels in the buildings, as well as availability and orientation of windows. However, these informations were only presented for reference purposes, as they were not included in further analysis on comfort temperatures. Data collection was performed by using instruments such as thermo-recorders and anemometers for climatic parameters, while questionnaire survey was performed simultaneously for subjective parameters. In the next chapter, results of both parameter groups are presented in detail.
CHAPTER 4
EVALUATION OF RESULTS
4.1
Introduction
The dataset of this thermal comfort study is a combination of objective and subjective parameters, which were obtained through a series of field measurements and surveys, as explained in the previous chapter. In this chapter, the data is presented in three sections: descriptive measures, results of indoor thermal measurements, and results of questionnaire survey. Then the last section of this chapter presents predicted mean votes and percentage of dissatisfied, which are estimated based on six variables affecting thermal comfort.
4.2
Descriptive Measures
For a better understanding on thermal comfort studies, personal information is essential to note; since recent field studies in tropical climate are partly caused by the premise that geographical factor, which is considered as part of individual characteristics, might affect comfort temperature due to adaptation (Nicol and Humphreys, 2002). A total of 350 office building occupants participated in this study. Personal information related to thermal comfort studies has been collected from them, while the identity of each respondent remains confidential. One of the main personal information is gender distribution, as shown in Figure 4.1. In Singapore and Japan, most of respondents are female, making up 64% and 53% of respondents respectively.
74 Conversely, male respondents are the majority in Indonesia (54%) and Malaysia (59%). Nevertheless, the distribution is quite even in all investigated countries.
Figure 4.1
Gender distribution by country (n refers to number of sample)
The following subsections discuss respondents’ personal information, namely demographic distribution, nationality, clothing insulation, and metabolic rates. The latter two were included in estimation of PMV and PPD. For more reference, additional demographic information such as extended results from Japanese respondents are presented in Appendix C.
4.2.1
Demographic distribution
The demographic information collected from respondents in this study is mainly age and gender, which was collected in all investigated countries. While nationality was also collected, this data will be presented in the next section. Respondents’ weight, height, and body surface area, which is estimated based on the former two parameters, were also collected in all locations except in Singapore. Since all the investigated buildings are offices, the age range of respondents is only within working age group; 15 to 64 years old (OECD, 2016). A summary of demographic information in all investigated countries are as shown in Table 4.1.
75
Table 4.1 : Distribution of age, weight, height, and body surface area Country Singapore
Indonesia
Malaysia
Japan
Variable n Min. Max. Mean S.D. n Min. Max. Mean S.D. n Min. Max. Mean S.D. n Min. Max. Mean S.D.
Age (year) 14 25 55 37 11 52 22 53 33 10 130 21 59 29 6 108 20 64 40 11
Weight (kg)
Height (m)
AD (m2)
54 40.0 95.0 62.4 13.2 126 42.0 135.0 68.9 16.7 92 38.0 97.0 60.4 12.1
53 1.47 1.80 1.63 0.09 128 1.15 1.97 1.65 0.11 111 1.47 1.85 1.66 0.09
53 1.30 2.02 1.67 0.18 125 1.19 2.44 1.75 0.23 92 1.29 2.15 1.68 0.19
Note: AD : DuBois body surface area (m2), n : Number of sample, Min.: Minimum, Max.: Maximum, S.D.: Standard deviation. In Singapore and Indonesia, the average age of respondents was within thirties years old. The average in Malaysia is the lowest at 29 years old, which might be due to a large number of respondents being postgraduate research students. Conversely, the average age of respondents in Japan is 40 years old. This could be related to ‘aging of Japan’ phenomenon, explaining the high proportion of elderly citizen in the country (Ministry of Internal Affairs and Communication Statistics Bureau, 2014). However, the respondents’ age range is also the widest in Japan, playing host to youngest (20 years old) and eldest (64 years old) respondents in this study. The bar chart in Figure 4.2 shows similar trends of occupants’ age in Singapore, Indonesia, and Malaysia; where the highest proportion of respondents are from the youngest age group in their twenties (≤ 29 years old) and steadily decreases in older age groups. In Japan, the proportion of age groups are quite similar between repondents in their twenties, thirties, and forties, while proportions of the two eldest age groups are the lowest.
76
Figure 4.2
Distribution of age by gender of respondents in each country
Based on all data in each investigated location, mean body weight of respondents of Malaysia of 68.9 kg is the highest compared to other locations, while the lowest mean body weight of 60.4 kg belongs to respondents in Japan, as shown in Table 4.1. Despite of low mean body weight, mean body height of respondents from Japan is 1.66 m, the highest compared to those in Malaysia and Indonesia. The latter actually has lowest mean body height of 1.63 m. Nevertheless, the range in average body height and weight between investigated countries is quite small. The error bar graph in Figure 4.3 showed that the data is very centralized and not affected by the outliers. Using a 95% confidence interval, it can be revealed that 95% of the data lies within the ranges shown.
(a)
(b)
Figure 4.3 Mean values of respondents’ demographic information with 95% confidence interval a) Weight, b) Height In terms of body height and weight distribution between genders, the trend of occupants in Indonesia, Malaysia, dan Japan is generally similar. Referring to gender-
77 separated data on Figure 4.3, it is apparent that both mean body weight and height of male respondents are higher than female respondents in all investigated countries.
As explained in chapter 3, body surface area (AD) in this study was estimated based on body height and weight using Dubois equation (DuBois and DuBois, 1916; Cornell University School of Medicine, 2000). Since the mean of both variables are highest for respondents in Malaysia, it follows that the mean AD of these respondents is also the highest at 1.75 m2, as shown in Table 4.1. The mean AD in Japan and Indonesia are quite similar at 1.68 and 1.67 m2 respectively.
4.2.2
Nationality distribution
All respondents from Indonesia and Singapore bear the same nationality as the country they reside. This is also true for respondents from Japan, except for one respondent with Australian nationality. In Malaysia, while all of respondents of offices in Shah Alam are all Malaysians, some of respondents from UTM Kuala Lumpur campus came from other countries, such as Iran, India, Myanmar, Pakistan, Egypt, and Palestine. Frequency and percentage of respondents’ nationality in each investigated country are shown in Table 4.2. Table 4.2 : Nationality distribution in each investigated country Investigated country Singapore Indonesia Malaysia
Japan
Nationality of respondent Singaporean Indonesian Malaysian Indonesian Indian Iran Myanmar Egyptian Pakistani Palestinian Japanese Autralian
Frequency 14 54 109 10 3 3 2 1 1 1 126 1
Percentage (%) 100.0 100.0 83.8 7.7 2.3 2.3 1.5 0.8 0.8 0.8 99.2 0.8
By combining all respondents’ nationality regardless of survey location in Figure 4.4, 38.8% of respondents are Japanese, 33.5% are Malaysian, 19.7% are
78 Indonesian, 4.3% are Singaporean, while the remaining 3.7% are the rest of nationals as listed. Based on this finding, the thermal comfort analysis was done separately according to the country where the field survey was held, i.e. Singapore, Indonesia, Malaysia, and Japan. The number of respondents with nationality other than mentioned countries is relatively small. It is presumed that they have adapted to their respective working environment.
Figure 4.4
4.2.3
Distribution of respondents' nationality
Clothing Insulation and Metabolic rates
Clothing insulation values were investigated through questionnaire, while direct observation was also required for general check during surveys. The averages of the clothing insulation (Icl) values in each location were closely related, as seen in Table 4.3. Example of typical clothing ensemble worn by respondents in Japan and Malaysia is shown in Figure 4.5. The highest mean Icl of 0.61 clo was recorded in the CL mode in Malaysia, whereas the lowest was 0.52 clo in the FR mode in Japan. Working outfits with lowest clothing insulation value was worn by a Japanese female respondent, with 0.28 clo from combination of short sleeve thin T-shirt, thin knee length skirt, stockings, and sandals. Meanwhile the highest clothing insulation value was demonstrated by a Malaysian male respondent, with 1.23 clo from combination of short sleeves t-shirt, long sleeves shirt, thick long trousers, calf-length socks, shoes, and jacket.
79
(a)
(b)
(c)
(d)
Figure 4.5 Typical clothings worn by respondents a) Male in Japan, b) Female in Japan, c) Male in Malaysia, d) Female in Malaysia
Previous studies in Japan (Goto et al., 2007; Tanabe et al., 2013) have shown that Japanese people tend to adjust their clothing according to the outdoor temperature from the previous day. However, such a tendency might not apply to the inhabitants of hot-humid climate countries. While the constant weather throughout the year in Malaysia could be the reason for the lack of correlation between the outdoor temperature and clothing adjustment. Icl values were also highest here, despite a higher outdoor temperature compared with that of Japan. This might be caused by high air conditioning usage in office buildings in Malaysia. It is clear that clothing selection is not based merely on climatic conditions, but also on cultural and social norms. In Japan, the Cool Biz campaign has tried to eliminate other such factors by encouraging office workers to wear thermally comfortable clothes during summer (Haneda, 2010).
80 Table 4.3 : Distribution of occupants' clothing insulation values Country
Mode
Singapore CL Indonesia FR MM CL Malaysia CL Japan FR CL
n 14 159 150 91 1105 36 402
Clothing Insulation (clo) Min. Max. Mean 0.31 0.74 0.57 0.38 0.90 0.58 0.37 0.98 0.59 0.39 1.08 0.52 0.30 1.23 0.61 0.36 0.70 0.52 0.28 1.05 0.55
S.D. 0.12 0.14 0.15 0.13 0.16 0.09 0.12
Note: FR : Free running, MM: Mixed-mode, CL: Cooling ventilation mode, n : Number of sample, Min.: Minimum, Max.: Maximum, S.D.: Standard deviation. The graphs in Figure 4.6 show that there is not much difference in clothing insulation between genders. The only striking high percentage is in Singapore, with majority of female respondents wore 0.51 – 0.70 clo during the survey; although the number of respondents are considered small compared to other locations. It is noticeable that in Malaysia and Indonesia, where most respondents are Muslim, many female respondents always wear head scarf (hijab) and long sleeves clothes. This might contribute to the high mean clothing insulation value in Malaysia, although clothing ensembles with the highest insulation value in this study was worn by a male respondent in Malaysia. Clothing insulation value of hijab is estimated as 0.05, based on previous laboratorial study about typical Arabian Gulf clothing ensembles using thermal manikins (Al-ajmi et al., 2008).
81
Clothing insulation, Icl (clo)
Figure 4.6 country
Distribution of clothing insulation value by occupants' gender in each
Metabolic rates of office occupants in Malaysia, Indonesia, and Singapore are assumed to be 1.2 met, since the activity is uniform, working in seated position. In Japan, metabolic rates was interpreted from the activity mentioned in questionnaire. There are some variations in the occupants’ responses, as seen in Figure 4.7, but the mean of metabolic rates in each ventilation mode in Japan are quite similar with each other: 1.25 met (S.D. : 0.40 met) for FR mode and 1.31 met (S.D.: 0.42 met) for CL mode.
Figure 4.7
Metabolic rate of Japanese respondents
82 4.3
Climatic Measurement Results
The results from the field measurements and survey were compiled and the mean values for each parameter were obtained for overview. The average indoor air temperature displayed in Table 4.4 was measured during the field measurement period only, and the result shows that the highest Ta was 26.7 °C in the FR mode in Indonesia. This value was quite similar to the temperatures that was measured in FR mode in Japan, which was 26.6 °C. In contrast, the lowest Ta was 23.1 °C measured in Singapore, where the lowest values for the other thermal indices, namely, Tg, Tmrt, and Top, were also recorded. In all the other locations, Tg was quite similar to Ta, except for MM at 27.1 °C, which was the highest Tg, despite its Ta being lower than those in the FR modes. In all the locations, the values for Tmrt and Top were all similar to that of Tg.
The highest relative humidity (RH) was 65% in Singapore in the CL mode, whereas the lowest was 47% in Indonesia in the same mode. In addition to RH, the absolute humidity (AH) was estimated as the ratio of water vapour (gram) for each kilogram of dry air. The lowest AH was 9.7 gv/kgda in the CL mode in Indonesia, and the highest was 13.7 gv/kgda in the FR mode, also in Indonesia. The second highest AH at 13.4 gv/kgda was recorded in the FR mode in Japan. The average air velocities (Va) were quite similar in all the locations, ranging between 0.10 and 0.20 m/s, which is a normal condition for an indoor setting.
Table 4.4 : Average values for climatic parameters in each country with different ventilation modes Country
Mode
Var.
Ta (C)
Tg (C)
Tmrt (C)
Top (C)
RH (%)
AH (gv/kgda) Va (m/s)
Malaysia
CL (n=1115) FR (n=159) MM (n=150) CL (n=91) CL (n=56) FR (n=37) CL (n=418)
Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D. Mean S.D.
24.0 1.7 26.7 0.2 26.5 1.3 25.5 0.4 23.1 1.3 26.6 0.3 26.1 0.8
24.4 1.7 26.7 0.2 27.1 1.3 25.8 0.4 23.2 1.3 26.5 0.3 25.9 0.7
24.8 1.8 26.7 0.2 27.8 1.2 26.2 0.5 23.3 1.4 26.5 0.3 25.7 0.9
24.4 1.7 26.7 0.2 27.2 1.3 25.9 0.4 23.2 1.3 26.5 0.8 25.9 0.7
58.2 5.9 62.2 2.3 54.3 4.3 47.3 2.7 64.9 4.2 61.6 0.4 56.7 6.1
10.9 1.3 13.7 0.5 11.8 0.9 9.7 0.5 11.6 1.6 13.4 1.6 12.0 1.5
Indonesia
Singapore Japan
0.23 0.09 0.07 0.01 0.17 0.03 0.13 0.03 0.06 0.03 0.22 0.14 0.15 0.07
Note: CL: Cooling, FR: Free-running, MM: Mixed-mode, Ta: Indoor air temperature, Tg: Globe temperature, Tmrt: Mean radiant temperature, Top: Operative temperature, RH: Indoor relative humidity, Va: Air velocity, AH: Indoor absolute humidity, n: Number of sample, S.D.: Standard deviation
83
84 In following subsections, each climatic variable is presented and discussed in detail. The variables are demarcated into four parts: outdoor air temperature, indoor temperature, indoor humidity, and indoor air velocity. Result of another thermal environment parameter measured in this study, i.e. indoor surface temperature, is summarized in Appendix D.
4.3.1 Outdoor air temperature As explained in chapter 3 section 3.5, outdoor temperature (To) was recorded simultaneously with indoor climatic data. Outdoor temperature with time interval between 10 seconds (Singapore and Indonesia) to 10 minutes (Malaysia and Japan) was taken as an average value for each measurement session. Table 4.5 presents the average To in each case study, separated between morning and afternoon session. For comparison, running mean temperature (Trm) and daily mean outdoor temperature (Tod) from respective survey days, as well as monthly mean outdoor temperature (Tom) from respective survey month are also presented.
The average To in all investigated location was always a bit higher in the afternoon, compared to morning session, with exception in FR mode in Japan which had the same average To in morning and afternoon. The highest mean To was measured in Malaysia, at 31.8 C in the afternoon and slightly lower in the morning at 30.6 C. The lowest To was recorded in Japan, on CL mode survey days at 22.7 C in the morning. The second lowest was on FR mode of the same country, at 23.3 C in both sessions. It is quite interesting to note that average To on both FR and CL mode in Japan is actually similar, while the occupants actually decided to use either one of ventilation mode as they feel suited with the weather. However, this might be contributed by the small number of survey days, since the lowest Trm, Tod, and Tom were on FR mode in Japan, although they were only slightly lower than those on CL mode in the same country.
85 Table 4.5 : Distribution of outdoor temperature during survey and comparison with running mean, daily mean, and monthly mean outdoor air temperature To Country
Mode
Singapore
CL
Indonesia
FR
MM
CL
Malaysia
CL
Japan
FR
CL
Morning (10:00 – 12:00) n Mean S.D. n Mean S.D. n Mean S.D. n Mean S.D. n Mean S.D. n Mean S.D. n Mean S.D.
5 26.0 0.5 5 28.6 0.4 3 26.8 0.4 48 30.6 1.9 1 23.3 0.0 21 22.7 2.4
Afternoon (14:00 – 16:00) 8 28.8 1.0 5 26.5 0.9 5 28.9 2.3 3 28.4 0.5 50 31.8 2.3 3 23.3 0.9 20 24.3 3.3
Trm
Tod
8 27.4 0.4 5 23.9 0.0 5 23.3 0.8 3 22.7 0.5 50 28.7 0.6 37 22.1 0.9 7 22.7 0.7
8 27.1 0.9 5 23.6 0.8 5 23.8 2.5 3 21.0 0.0 50 28.8 1.0 37 21.0 0.8 7 22.0 2.4
Tom 1 26.9 0.0 1 22.0 0.0 2 22.8 0.4 1 22.0 0.0 3 28.8 0.2 2 22.6 0.1 2 22.7 0.1
Note: CL: Cooling, FR: Free-running, MM: Mixed-mode, To: Outdoor air temperature, Trm: Running mean temperature, Tod: Daily mean outdoor air temperature, Tom: Monthly mean outdoor air temperature, n: Number of sample, S.D.: Standard deviation All values of the three outdoor temperatures, Trm, Tod, and Tom, are almost similar to each other. The highest gap was between Trm and Tom by 1.9 C on FR mode in Indonesia, where Tom was lower. Compared to session-separated To dataset, the other three outdoor temperatures are generally low. This is actually an expected pattern since Trm, Tod, and Tom are estimated based on 24 hours measurement data, therefore they also include lower night time temperatures. The comparison for average value of all the To data, with Trm, Tod, and Tom in each investigated location is indicated with error bars in Figure 4.8.
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Figure 4.8 Mean outdoor temperature, daily mean outdoor temperature, monthly mean outdoor temperature, and running mean temperature in each country Overall recorded outdoor temperatures during survey days are normally distributed, as indicated in Figure 4.9.
Figure 4.9
Outdoor temperature frequency proportions during survey in each country
In specific weather conditions, i.e. cloudy, moderate, rainy, and sunny, the mean To remained relatively unaffected in Singapore, Indonesia, and Malaysia. On the contrary, the mean To in Japan changed according to the specific weather. The descriptive statistics on Table 4.6 indicated that the minimum and maximum To were lower when it was ‘rainy’ weather in all investigated locations.
87 Table 4.6 : Outdoor temperature variations in specific weather Country Singapore Indonesia
Malaysia
Japan
Weather condition Cloudy Rainy Cloudy Rainy Sunny Cloudy Rainy Sunny Cloudy Moderate Rainy Sunny
n 5 3 5 6 15 19 4 75 7 12 17 15
Outdoor temperature, To (°C) Min. Max. Mean 28.5 29.6 29.1 27.0 30.3 28.2 26.3 31.2 27.9 24.8 29.1 27.2 25.5 31.1 27.6 27.4 35.1 31.3 23.6 32.9 30.9 27.9 35.3 30.7 17.3 26.1 23.5 22.9 26.6 24.6 17.1 25.6 21.2 23.3 30.1 26.2
S.D. 0.4 1.8 2.0 1.6 1.7 2.3 2.1 2.2 3.0 1.3 3.2 2.5
Note: n : Number of sample, Min.: Minimum, Max.: Maximum, S.D.: Standard deviation.
4.3.2 Indoor temperature There are four thermal indices used in this study to describe indoor temperature, two of them are measured directly: Ta and Tg; while the other two, Tmrt and Top, are estimated using the methodology explained in section 3.6.1. The distribution of Ta in each investigated location is presented in frequency histogram in Figure 4.10. These frequencies are estimated based on number of responses collected by the time of respective mean air temperatures are recorded, in each measurement session. Indoor air temperature, as expected, is greatly affected by the type of ventilation mode used by the time of measurement. The range of Ta in CL mode in Singapore was really low, between 21 to 25 °C, although they have very low frequency compared to other locations. The highest mean Ta was recorded in CL mode in Malaysia, although the data lie outside of a normal distribution. The highest recorded frequency in Malaysia was at 24 °C. Another high range of mean Ta was measured in MM ventilation in Indonesia, spread between 24 to 28 °C, where the data range was quite wide and not normally distributed. Meanwhile the range of average Ta in FR mode in both Indonesia and Japan was quite similar, between 26 to 27 °C.
88
Indoor air temperature, Ta (°C)
(a)
(c)
(b)
(d)
Figure 4.10 Histogram of indoor air temperature in a) Singapore, b) Indonesia, c) Malaysia, d) Japan
It is apparent that indoor air temperature differed between ventilation modes. For reference in analysis, Ta in each investigated location is also plotted against To, as shown in Figure 4.11. Outdoor temperature was quite constant in most of investigated location, with fluctuations of around 5 °C. A wider range of outdoor temperatures was found in CL mode in Malaysia and Japan, with more than 10 °C difference between the lowest and highest mean To recorded. This wide range is caused by weather changes during measurement period. It also occurred in MM ventilation in Indonesia, where To in the afternoon sessions are either higher than morning session when there is no rain, and vice versa, lower than morning session if it rains in the afternoon, as recorded during field measurement (see Table 4.6).
89
(a)
(b)
(c)
(d)
Figure 4.11 Scatterplot of indoor air temperatures against outdoor temperatures for each measurement session in a) Singapore, b) Indonesia, c) Malaysia, d) Japan Distribution of the other three thermal indices is mostly similar with Ta, although in some cases, a certain pattern could be noticed. Comparison between the four thermal indices is as indicated by error bars in Figure 4.12. In both FR modes in Indonesia and Japan, the four thermal indices showed no differences. Meanwhile in all other investigated locations, there is a similar pattern, which is suspected to be caused by the HVAC used in these offices. Globe temperatures in principal are the weighted mean of ambient and radiant temperature, since it is assumed to measure the temperature on ‘equilibrium’, when the heat gained and lost through convection and radiation are in balance (ASHRAE, 2001). During the day with warm weather, this temperature is usually slightly higher than air temperature. The only location where Tg was recorded lower than Ta in this study is in CL mode in Japan.
Since Tmrt is estimated based on Ta, Tg, Va, and diameter of globe thermometer, it will be affected as the value of the latter three variables change. During warm days, the value of Tmrt tends to be higher than Tg, and vice versa in cold days. The value of Top lies between that of Ta and Tmrt, because it is the weighted mean of both temperatures. This difference is not visible in CL mode in Singapore, which could be attributable to the small sample.
90
(a)
(b)
(c)
(d)
Figure 4.12 Mean temperature in four thermal indices with 95% confidence interval a) Singapore, b) Indonesia, c) Malaysia, d) Japan It is important to determine the relation of one thermal index to another due to the various indices used in this study. Ta was found to be strongly correlated with the other temperature indices as indicated in Table 4.7. This strong correlation could imply that to a significant degree, all the thermal indices were in effect measuring the same factor. Therefore, it is feasible to use any of these indices in the analysis. In light of this finding, Top was mainly used as the thermal index for further discussion in this study, while the other indices were used only for reference purposes. Table 4.7 : Regression equations and correlation coefficients of air temperature and other thermal indices Mode FR (n=219) MM (n=150) CL (n=1680)
Items
Ta: Tg
Ta: Tmrt
Ta: Top
Eq. R Eq. R Eq. R
Tg = 1.03 Ta − 0.89 0.99 Tg = 0.97 Ta +1.44 1.00 Tg = 0.92 Ta +2.16 0.97
Tmrt = 1.08 Ta −2.22 0.90 Tmrt = 0.91 Ta +3.63 0.98 Tmrt = 0.8 Ta +5.36 0.84
Top = 1.04 Ta −1.11 0.97 Top = 0.96 Ta +1.82 1.00 Top = 0.9 Ta +2.67 0.96
Note: CL: Cooling, FR: Free-running, MM: Mixed-mode, Ta: Indoor air temperature (°C), Tg: Globe temperature (°C), Tmrt: Mean radiant temperature (°C), Top: Operative temperature (°C), Eq.: Equation, R: Correlation coefficient, n: Number of sample. All correlation coefficients are significant (p
