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Title: Interactions and comprehensive effect of indoor environmental quality factors on occupant satisfaction

Abstract

In this study, eight subjects were exposed in a simulated office to 31 combinations of indoor environmental conditions, assigned by orthogonal design and uniform design. Conditions comprised variations of Predicted Mean Vote (PMV), illuminance, sound pressure and CO 2 concentration (independent of a consistent ventilation rate) as indicators of thermal, lighting, acoustic and indoor air quality. Participant satisfaction with each of the four factors and with overall environmental conditions were measured with a questionnaire. Multiple interactions were detected with a partial correlation analysis and regression analysis. Results showed an adjusted effect of illuminance on perceived acoustic environment, a significant effect of the thermal environment on indoor air quality satisfaction, and a slight effect of sound pressure on indoor air quality satisfaction. Linear and geometric mean regression models were investigated for predicting overall satisfaction from the factor satisfaction scores. For the linear model, it was determined that multicollinearity among factor satisfaction levels may result in non-significant and biased estimated coefficients. The geometric mean regression model provides better prediction accuracy than the linear regression model with fewer coefficients, and accounts for the finding that the lowest satisfaction level with any environmental factor appears to drive overall satisfaction.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]
  1. Chongqing Univ., Chongqing (China)
  2. 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)
OSTI Identifier:
1580429
Alternate Identifier(s):
OSTI ID: 1570058
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Building and Environment
Additional Journal Information:
Journal Volume: 167; Journal Issue: C; Journal ID: ISSN 0360-1323
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Tang, Hao, Ding, Yong, and Singer, Brett. Interactions and comprehensive effect of indoor environmental quality factors on occupant satisfaction. United States: N. p., 2020. Web. doi:10.1016/j.buildenv.2019.106462.
Tang, Hao, Ding, Yong, & Singer, Brett. Interactions and comprehensive effect of indoor environmental quality factors on occupant satisfaction. United States. doi:10.1016/j.buildenv.2019.106462.
Tang, Hao, Ding, Yong, and Singer, Brett. Wed . "Interactions and comprehensive effect of indoor environmental quality factors on occupant satisfaction". United States. doi:10.1016/j.buildenv.2019.106462.
@article{osti_1580429,
title = {Interactions and comprehensive effect of indoor environmental quality factors on occupant satisfaction},
author = {Tang, Hao and Ding, Yong and Singer, Brett},
abstractNote = {In this study, eight subjects were exposed in a simulated office to 31 combinations of indoor environmental conditions, assigned by orthogonal design and uniform design. Conditions comprised variations of Predicted Mean Vote (PMV), illuminance, sound pressure and CO2 concentration (independent of a consistent ventilation rate) as indicators of thermal, lighting, acoustic and indoor air quality. Participant satisfaction with each of the four factors and with overall environmental conditions were measured with a questionnaire. Multiple interactions were detected with a partial correlation analysis and regression analysis. Results showed an adjusted effect of illuminance on perceived acoustic environment, a significant effect of the thermal environment on indoor air quality satisfaction, and a slight effect of sound pressure on indoor air quality satisfaction. Linear and geometric mean regression models were investigated for predicting overall satisfaction from the factor satisfaction scores. For the linear model, it was determined that multicollinearity among factor satisfaction levels may result in non-significant and biased estimated coefficients. The geometric mean regression model provides better prediction accuracy than the linear regression model with fewer coefficients, and accounts for the finding that the lowest satisfaction level with any environmental factor appears to drive overall satisfaction.},
doi = {10.1016/j.buildenv.2019.106462},
journal = {Building and Environment},
number = C,
volume = 167,
place = {United States},
year = {2020},
month = {1}
}

Journal Article:
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This content will become publicly available on January 1, 2021
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