Thermal discomfort is one of the main triggers for occupants’ interactions with components of the built environment such as adjustments of thermostats and/or opening windows and strongly related to the energy use in buildings. Understanding causes for thermal (dis-)comfort is crucial for design and operation of any type of building. The assessment of human thermal perception through rating scales, for example in post-occupancy studies, has been applied for several decades; however, long-existing assumptions related to these rating scales had been questioned by several researchers. The aim of this study was to gain deeper knowledge on contextual influences on the interpretation of thermal perception scales and their verbal anchors by survey participants. A questionnaire was designed and consequently applied in 21 language versions. These surveys were conducted in 57 cities in 30 countries resulting in a dataset containing responses from 8225 participants. The database offers potential for further analysis in the areas of building design and operation, psycho-physical relationships between human perception and the built environment, and linguistic analyses.
|Publication status||Published - 2019 Dec 1|
Bibliographical noteFunding Information:
This project was conducted under the framework of IEA EBC Annex 69. M.Sch., S.B., and K.S-E. acknowledge funding by the Heidelberg Academy of Sciences and Humanities within the project “Thermal comfort and pain”. M.Sch. is thankful to Prof. F. Nicol to share the information on participation via NCEUB, Prof. A. Wagner, Prof. G. Vrachliotis, and Dr. M. Krause from Karlsruhe Institute of Technology for their support in distributing questionnaires. A.A.-Z. would like to thank all the students from University of Technology-Iraq/Mechanical Engineering Department, who participated by providing answers to the questionnaire. F.A.-A. and the research related to Jordan was supported by M.Sch. H.A.-K. is thankful to Dalal Al-Khatri, Noor Alhuda Al-Saqri, and Maryam Al-Bartamani for their contribution in distributing the questionnaires. E.Am. is thankful to Eleni Alexandrou of the National Technical University of Athens for her contribution in distributing the questionnaires. B.C. is thankful to the National Natural Science Foundation of China (No. 51521005 and No. 51678330). C.C. is thankful to National Research Foundation of Korea (No. NRF-2017R1A2B4012122) J-H.C. would like to express gratitude to the USC students who volunteered for the data collection, and a special thanks to Ms. Zhihe Wang, who provided a technical assistance to compile the acquired dataset. S.D.-A. is thankful for the funding support of the TEP-130 R&D group from the US. L.P.E.would like to thank IITM, India for the support and DAAD for the scholarship. G.G. is thankful to Andrés Chico, Freddy Ordoñez, Jesús López (Escuela Politécnica Nacional – EPN), Ricardo Narváez (Universidad Central del Ecuador), Guillermo Soriano (Escuela Superior Politécnica del Litoral - ESPOL), Daniel Quiroz (Universidad Regional Amazónica Ikiam) and Stalin Guamán (Universidad Regional Amazónica Ikiam). S.G. would like to thank the Sustainable Energy Research Group (energy.soton.ac.uk) for supporting this work. D.H. is thankful to all the students from Universiti Malaysia Sabah who participated in the present field study. She is also very grateful to all the lecturers who contributed directly and indirectly in the present study. R.T.H. would like to thank Prof Mahena Stief, Dr. Julia Sonnberger, Stefan Keller, Prof Markus Reppich and Prof Michael Krupp and all HSA students involved for their support. G.M.H. was supported by Research Councils UK (RCUK) Centre for Energy Epidemiology (EP/K011839/1) and UK Research and Innovation through the Centre for Research into Energy Demand Solutions, grant reference number EP/R 035288/1. Q.J. acknowledges the funding by Chalmers Energy Area of Advance and is thankful to Jan-Olof Dalenbäck, Holger Wallbaum, Björn Gross, and Ulrike Rahe. B.K. is thankful for Heatshield, under EU Horizon 2020 grant agreement No 668786 and the Ministerie van Defensie (SOLAR V1605). J.Ko. would like to thank Prof. Jørn Toftum for help with translation of the survey questionnaire A.K. is appreciative of the volunteer work of Yi-Cheng Lei, Eugene Leung, Bentley Rager, Rachel Rimmer, and Kelly Schoenborn. M.C.J.L. would thank for MOST Taiwan and NTCUST to support the funding and measure instruments. I.M-R. is thankful to Andrés Chico, Freddy Ordoñez, Jesús López (Escuela Politécnica Nacional – EPN), Ricardo Narváez (Universidad Central del Ecuador), Guillermo Soriano (Escuela Superior Politécnica del Litoral – ESPOL), Daniel Quiroz (Universidad Regional Amazónica Ikiam) and Stalin Guamán (Universidad Regional Amazónica Ikiam). I.R. is thankful to first year Architecture students of University of Moratuwa who volunteered for the data collection. M.Ok. would want to thank all the students from Imo State University, Owerri and Federal Polytechnic, Nekede who participated by providing answers to the questionnaire. M.Ol. would like to thank Tadeo Nedala and Menelik Tibikabire who helped administer the questionnaires. W.O. would like to thank Prof Bruce Lonnman for his support and all CUHK students involved. The study is supported by General Research Fund, Research Grant Council, Hong Kong (Project code: 14629516) and Vice-Chancellor’s One-off Discretionary Fund of the Chinese University of Hong Kong. A.L.P.A. would like to thank the Civil Engineering students of IFSC who volunteered for the data collection. M.I.R. is appreciative of the volunteer work of Yi-Cheng Lei, Eugene Leung, Bentley Rager, Rachel Rimmer, and Kelly Schoenborn. V.S.‘s involvement in the project was partially funded through the Special Study Program provided by The Faculty of Professions, University of Adelaide. D.T. would like to thank Vinnova (Sweden’s Innovation Agency) and her colleagues at the Division of Building Services Engineering for their support.
© 2019, The Author(s).
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Information Systems
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences