@article{36c4ccf5d8184755b94987412f03e0c0,
title = "Artificial neural network models using thermal sensations and occupants{\textquoteright} behavior for predicting thermal comfort",
abstract = "It is important to create comfortable indoor environments for building occupants. This study developed artificial neural network (ANN) models for predicting thermal comfort in indoor environments by using thermal sensations and occupants{\textquoteright} behavior. The models were trained by data on air temperature, relative humidity, clothing insulation, metabolic rate, thermal sensations, and occupants{\textquoteright} behavior collected in ten offices and ten houses/apartments. The models were able to predict similar acceptable air temperature ranges in offices, from 20.6 °C (69℉) to 25 °C (77℉) in winter and from 20.6 °C (69℉) to 25.6 °C (78℉) in summer. The occupants{\textquoteright} behavior in multi-occupant offices was more complex, which would lead to a slightly different prediction of thermal comfort. Since the occupants of the houses/apartments were responsible for paying their energy bills, the comfortable air temperature in these residences was 1.7 °C (3.0℉) lower than that in the offices in winter, and 1.7 °C (3.0℉) higher in summer. The comfort zone obtained by the ANN model using thermal sensations in the ten offices was narrower than the comfort zone in ASHRAE Standard 55, but that obtained by the ANN model using behaviors was wider than the ASHRAE comfort zone. This investigation demonstrates alternative approaches to the prediction of thermal comfort.",
keywords = "Air temperature, Clothing level, Data collection, Indoor environment, Metabolic rate, Model training, Relative humidity",
author = "Zhipeng Deng and Qingyan Chen",
note = "Funding Information: The authors would like to thank Dr. Orkan Kurtulus of the Center for High Performance Buildings at Purdue University for his kind assistance in setting the building automation system in the Herrick Laboratories building. We would also like to thank all the occupants of the offices and apartments/houses for their participation and assistance in obtaining the data reported in this study. The research presented in this paper was supported by the Center for High Performance Buildings at Purdue University. The research presented in this paper was partially supported by the national key R&D project of the Ministry of Science and Technology , China, on “Green Buildings and Building Industrialization” through Grant No. 2016YFC0700500 . Funding Information: The authors would like to thank Dr. Orkan Kurtulus of the Center for High Performance Buildings at Purdue University for his kind assistance in setting the building automation system in the Herrick Laboratories building. We would also like to thank all the occupants of the offices and apartments/houses for their participation and assistance in obtaining the data reported in this study. The research presented in this paper was supported by the Center for High Performance Buildings at Purdue University. The research presented in this paper was partially supported by the national key R&D project of the Ministry of Science and Technology, China, on “Green Buildings and Building Industrialization” through Grant No. 2016YFC0700500. Publisher Copyright: {\textcopyright} 2018 Elsevier B.V.",
year = "2018",
month = sep,
day = "1",
doi = "10.1016/j.enbuild.2018.06.060",
language = "English",
volume = "174",
pages = "587--602",
journal = "Energy and Buildings",
issn = "0378-7788",
publisher = "Elsevier BV",
}