Adaptive behavior and different thermal experiences of real people: A Bayesian neural network approach to thermal preference prediction and classification

• Published in: Building and environment Vol. 198; no. C; p. 107875
• Main Authors: Ma, Nan, Chen, Liang, Hu, Jian, Perdikaris, Paris, Braham, William W.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-07-2021; Elsevier BV; Elsevier
• Subjects: Algorithms; Bayesian analysis; Bayesian inference; Bayesian neural network; Classification; Indoor environments; Mathematical models; Modelling; Neural networks; Occupant behavior; Performance prediction; Predicted Mean Vote index; Prediction models; Preferences; Thermal comfort; Thermal preference; Uncertainty; Window system; Window systems; Thermal comfort; Occupant behavior; Window system; Bayesian inference; Bayesian neural network; Thermal preference
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2021.107875

Various observed and unquantifiable factors affect the thermal comfort of occupants in indoor environments and can lead to high uncertainty in the prediction and classification of their thermal preferences. The behavioral adaptation of occupants, by operating window systems for example, changes their thermal experience and expectations and therefore contributes to even higher prediction uncertainty. In this study, we applied a Bayesian neural network (BNN) algorithm to build a predictive model for occupant thermal preference using the ASHRAE Global Thermal Comfort Database II. The Bayesian method allows us to synthesize prior knowledge and available measurements into a unified modeling framework. It also offers a way to express and quantify uncertainty. Here we have performed a systematic study to test the efficiency and robustness of different BNN model configurations. The results show that the BNN model outperforms conventional thermal comfort models such as Predicted Mean Vote (PMV) and adaptive comfort model. The BNN model tends to produce more confident “prefer cooler” predictions with high possibility and low uncertainty. In contrast, the BNN model produces less certain predictions for “prefer no change” and “prefer warmer” across all occupants. Our findings suggest that linking occupants’ subjective evaluation measures and window opening/closing behavior to thermal comfort modeling effectively improves predictive performance.
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•We apply Bayesian neural networks (BNN) for thermal preference prediction.•We use occupant window opening/closing behavior and subjective measures as features.•The BNN has superior ability in the “prefer cooler” classification.•The Bayesian inferred thermal preference model outperforms PMV and adaptive model.•Occupant window behavior and subjective measures are strong comfort predictors.