Bayesian updates for indoor thermal comfort models

• Published in: Journal of Building Engineering Vol. 29; p. 101117
• Main Authors: Mui, K.W., Tsang, T.W., Wong, L.T.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2020
• Subjects: Acceptance; Bayesian updating; Prediction; Thermal comfort; Bayesian updating; Thermal comfort; Acceptance; Prediction
• ISSN: 2352-7102; 2352-7102
• DOI: 10.1016/j.jobe.2019.101117

Achieving thermal comfort through sustainable indoor design is an increasing concern. Thermal comfort modelling is crucial for achieving building energy saving. This study reviews and categorizes major developments and trends in the field of thermal comfort research in recent years. Discrepancies between actual and predicted results of thermal sensation and thermal satisfaction suggests a performance gap in Fanger's model. Based on the current research gaps identified, a practical solution is proposed to improve the reliability of thermal comfort predictions. Two Bayesian updating protocols, namely individual updating and global updating, are put forward and the use of Bayesian approach to systemically update current thermal comfort beliefs with openly available field data is demonstrated. Besides being a practical tool for modelling thermal comfort using the best information available (i.e. existing models and field survey data), the proposed Bayesian updating provides an achievable solution to the present challenges in establishing a reliable thermal comfort prediction model.
•Thermal comfort review found that research into the improvement and development of thermal acceptance model is limited.•Discrepancy between model and actual field data greatly affects the energy saving implication in thermal comfort research.•Individual and global updating are proposed to minimize the performance gap using Bayesian approach.•Bayesian thermal comfort model provides a reliable prediction model based on best information available.