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Prediction of occupant thermal state via infrared thermography and explainable AI

Zhang, S., Yao, R. ORCID: https://orcid.org/0000-0003-4269-7224, Wei, H. ORCID: https://orcid.org/0000-0002-9664-5748 and Li, B. (2024) Prediction of occupant thermal state via infrared thermography and explainable AI. Energy and Buildings, 312. 114153. ISSN 0378-7788

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To link to this item DOI: 10.1016/j.enbuild.2024.114153

Abstract/Summary

Accurate and real-time assessment of occupant thermal comfort can provide a solid foundation for efficient air conditioning operations. Existing studies already show the feasibility of using contactless technologies for thermal comfort prediction assisted by machine learning algorithms. However, the lack of transparency in machine learning often weakens user trust. This study performs explainable AI analysis to explore the potential of infrared imaging in thermal comfort evaluation. Specifically, an investigation was carried out in a climatic chamber, and infrared cameras were used to collect facial temperature data. Five popular ensemble tree models were employed to construct prediction models, and explainable AI analysis was performed using SHAP (SHapley Additive exPlanations) theory. Results show that combining additional facial information can significantly improve the overall model performance, and certain facial attributes present high contributions based on SHAP values. Combining facial features with explainable AI provides a convincing basis for thermal comfort assessment. The high SHAP values of facial features can also contribute to finding selective occupants with low neutral voting rates, providing evidence for customized cooling or heating from building systems.

Item Type:Article
Refereed:Yes
Divisions:Science > School of the Built Environment > Energy and Environmental Engineering group
ID Code:116592
Uncontrolled Keywords:Thermal comfort, XGBoost, explainable AI, facial thermography
Publisher:Elsevier

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