4.6 Article

Adaption and Tolerance in Built Environment-An Evaluation of Environmental Sensation, Acceptance and Overall Indoor Environmental Quality (IEQ) in a Subtropical Region

期刊

SUSTAINABILITY
卷 15, 期 14, 页码 -

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MDPI
DOI: 10.3390/su151411094

关键词

adaption; tolerance; sensation; acceptance; indoor environment quality (IEQ)

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This study is the first to explore and quantify the relationship between environmental sensations and acceptance in the indoor environment. It investigates the sensation and acceptance of thermal comfort, indoor air quality, aural comfort, visual comfort, and overall indoor environmental quality (IEQ). The study develops machine learning models to predict overall IEQ acceptance based on individual IEQ domains, considering the influence of environmental adaptation and tolerance. The study has practical implications for improving building performance and provides insights into the relationship between environmental sensations and occupants' acceptance, which should be considered in building design and operation.
The relationship between environmental sensations and acceptance in the indoor environment has yet to be fully explored or quantified. This study is the first in the literature that examines these relationships in thermal comfort, indoor air quality, aural comfort, visual comfort, and overall indoor environmental quality (IEQ). Using a regional IEQ database, the relationship between occupants' sensation and acceptance of individual environmental aspects was investigated. The results suggest that building occupants had high tolerances towards indoor air quality and aural and visual discomforts, while cold sensations tended to elicit environmental discomfort. Furthermore, the study developed machine learning models with imbalanced data treatment to predict overall IEQ acceptance based on both sensation and acceptance of individual IEQ domains. These models accounted for the influence of environmental adaptation and tolerance on overall IEQ satisfaction determination. They accurately predicted unseen data, indicating high model generalizability and robustness. Overall, the study has practical implications for improving building performance and provides insights to better understand the relationship between environmental sensations and occupants' acceptance, which should be considered in building design and operation.

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