Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

TitleLearning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States
Publication TypeJournal Article
Year of Publication2020
AuthorsZhe Wang, Tianzhen Hong
JournalRenewable and Sustainable Energy Reviews
Volume119
Pagination109593
Date Published01/2020
ISSN13640321
Keywordsactive learning, ASHRAE global thermal comfort database, bayesian inference, office buildings, temperature set-point, thermal comfort
Abstract

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

DOI10.1016/j.rser.2019.109593
Short TitleRenewable and Sustainable Energy Reviews