Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest

Establishing a predictive model for human thermal sensation serves as the fundamental theoretical basis for intelligent control of building HVAC systems based on thermal comfort. The traditional Predicted Mean Vote (PMV) model exhibits low accuracy in predicting human thermal sensation and is not well suited for practical applications. In this study, real thermal sensation survey data were collected and used to first analyze the discrepancy between PMV model predictions and actual human thermal sensation. Subsequently, a simple thermal sensation prediction model was developed using multiple linear regression. More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. Finally, the best-performing model was further optimized using Bayesian methods to enhance hyperparameter tuning efficiency and improve the accuracy of personalized human thermal sensation prediction.