Comparison of models for predicting winter individual thermal comfort based on machine learning algorithms. (1st May 2022) - Yang, Bin
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. Highlights: The impact of four algorithms on the performance of thermal comfort models. The models using environmental and biological data as input reached the highest accuracy. The maximum accuracy of optimization models using BO technology is 13.3%.