Comparison of models for predicting winter individual thermal comfort based on machine learning algorithms

Abstract

Machine learning-based human thermal comfort prediction is becoming increasingly popular as artificial intelligence (AI) technologies advance. Human skin temperature is a critical physiological factor in thermal comfort research. In winter, we developed a thermal comfort prediction model based on skin temperature and environmental factors. During the experimental phase, the superior performance of the proposed method is demonstrated through a comparative study that includes four different state-of-the-art models, including Support Vector Machine, Decision Tree, Ensemble Algorithms, and K-Nearest Neighbor. With all variables as inputs, the actual accuracy of the proposed thermal sensation vote (TSV) model prediction is 95.8%. In addition, the hyperparameters of machine learning algorithms were tuned using a personal classification model based on the Bayesian Optimization technique. This study demonstrates the model's capability of predicting individual thermal comfort.