Open AccessBayesian inference of thermal comfort: evaluating the effect of “well-being” on perceived thermal comfort in open plan offices
Author(s) -
Sarah Crosby,
Guy R. Newsham,
Jennifer A. Veitch,
Steven N. Rogak,
Adam Rysanek
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/609/4/042028
Subject(s) - thermal comfort , open plan , environmental quality , context (archaeology) , inference , architectural engineering , indoor air quality , quality (philosophy) , logistic regression , bayesian inference , bayesian probability , computer science , environmental science , engineering , machine learning , artificial intelligence , civil engineering , meteorology , environmental engineering , geography , political science , law , philosophy , archaeology , epistemology
The judgment of thermal comfort is a cognitive process which is influenced by physical, psychological and other factors. Prior studies have shown that occupants, who are generally satisfied with many non-thermal conditions of indoor environmental quality, are more likely to be satisfied with thermal conditions as well. This paper presents a novel approach that considers the effect of non-thermal building environmental design conditions, such as indoor air quality and noise levels, on perceived thermal comfort in open-plan offices. The methodology involves the use of Bayesian inference to relate the occupant’s thermal dissatisfaction in a building not only to thermal conditions and occupant metabolic factors (i.e., parameters of the original Fanger model), but also to measurable non-thermal metrics of indoor environmental quality. A Bayesian logistic regression approach is presented in this paper. The experimental context regards a prior indoor environmental quality measurement and evaluation study of 779 occupants of open-plan offices throughout Canada and the US. We present revised PMV-PPD curves for real-world offices that take into account both thermal and wellbeing IEQ parameters. The Bayesian inference analysis reveals that the occupant’s thermal dissatisfaction is influenced by many non-thermal IEQ conditions, such as indoor CO 2 concentrations and the satisfaction with the office lighting intensity.