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Title: Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning

Abstract

Here, we analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the assessment of the occupants' thermal experience. We found that the thermal sensation is the most frequently used metrics in Thermal Comfort Database II, followed by thermal preference, comfort and acceptability. The thermal sensation/thermal preference, thermal comfort/air movement acceptability and thermal comfort/thermal preference are the top three most dependent metrics pairs. A principal component analysis confirmed that the personal experience of thermal conditions in built environment is not a one-dimensional problem, but at least a two-dimensional problem, and suggested thermal sensation and thermal comfort should be asked in right-now surveys as the first two Principal Component are majorly constructed by thermal sensation and thermal comfort. To further confirm the predictive power of thermal sensation and comfort, we used logistic regression and support vector machine to predict thermal acceptability and thermal preference with thermal sensation and comfort. The prediction accuracy is 87% for thermal acceptability and 64% for thermal preference. The prediction error might be due to occupants' individual difference and people errors in answering survey. These findings could helpmore » the design of chamber experiments, field studies, and human-building interaction interfaces by shedding light on the choice of subjective thermal metrics to effectively and accurately collect information on occupants’ thermal experience.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. China Construction Science & Technology Group Co. Ltd., Beijing (China)
  3. National Univ. of Singapore (Singapore)
  4. Guangzhou Univ. (China)
  5. Tsinghua Univ., Beijing (China)
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
OSTI Identifier:
1607418
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Building Engineering
Additional Journal Information:
Journal Volume: 29; Journal Issue: C; Journal ID: ISSN 2352-7102
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; thermal comfort; subjective thermal metrics; ASHRAE global thermal comfort database II; machine learning; occupancy responsive control; principal component analysis

Citation Formats

Wang, Zhe, Wang, Jingyi, He, Yueer, Liu, Yanchen, Lin, Borong, and Hong, Tianzhen. Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning. United States: N. p., 2019. Web. https://doi.org/10.1016/j.jobe.2019.101120.
Wang, Zhe, Wang, Jingyi, He, Yueer, Liu, Yanchen, Lin, Borong, & Hong, Tianzhen. Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning. United States. https://doi.org/10.1016/j.jobe.2019.101120
Wang, Zhe, Wang, Jingyi, He, Yueer, Liu, Yanchen, Lin, Borong, and Hong, Tianzhen. Tue . "Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning". United States. https://doi.org/10.1016/j.jobe.2019.101120. https://www.osti.gov/servlets/purl/1607418.
@article{osti_1607418,
title = {Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning},
author = {Wang, Zhe and Wang, Jingyi and He, Yueer and Liu, Yanchen and Lin, Borong and Hong, Tianzhen},
abstractNote = {Here, we analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the assessment of the occupants' thermal experience. We found that the thermal sensation is the most frequently used metrics in Thermal Comfort Database II, followed by thermal preference, comfort and acceptability. The thermal sensation/thermal preference, thermal comfort/air movement acceptability and thermal comfort/thermal preference are the top three most dependent metrics pairs. A principal component analysis confirmed that the personal experience of thermal conditions in built environment is not a one-dimensional problem, but at least a two-dimensional problem, and suggested thermal sensation and thermal comfort should be asked in right-now surveys as the first two Principal Component are majorly constructed by thermal sensation and thermal comfort. To further confirm the predictive power of thermal sensation and comfort, we used logistic regression and support vector machine to predict thermal acceptability and thermal preference with thermal sensation and comfort. The prediction accuracy is 87% for thermal acceptability and 64% for thermal preference. The prediction error might be due to occupants' individual difference and people errors in answering survey. These findings could help the design of chamber experiments, field studies, and human-building interaction interfaces by shedding light on the choice of subjective thermal metrics to effectively and accurately collect information on occupants’ thermal experience.},
doi = {10.1016/j.jobe.2019.101120},
journal = {Journal of Building Engineering},
number = C,
volume = 29,
place = {United States},
year = {2019},
month = {12}
}

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