Publication Type

Journal Article

Version

publishedVersion

Publication Date

11-2022

Abstract

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using a smartwatch for 180 days. We collected more than 1080 field-based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of the time to want no change in their thermal environment despite completing 75% of these surveys at temperatures higher than 26.6°C. All but one personal comfort model had a median prediction accuracy of 0.78 (F1-score). Skin, indoor, near body temperatures, and heart rate were the most valuable variables for accurate prediction. We found that ≈250–300 data points per participant were needed for accurate prediction. We, however, identified strategies to significantly reduce this number. Our study provides quantitative evidence on how to improve the accuracy of personal comfort models, prove the benefits of using wearable devices to predict thermal preference, and validate results from previous studies.

Keywords

ecological momentary assessment, internet of things (IoT), machine learning, personal thermal comfort model, skin temperature

Discipline

Engineering | Environmental Sciences

Research Areas

Integrative Research Areas

Publication

Indoor Air

Volume

32

Issue

11

First Page

1

Last Page

16

ISSN

0905-6947

Identifier

10.1111/ina.13160

Publisher

Wiley

Additional URL

https://doi.org/10.1111/ina.13160

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