Modeling and Prediction of Human Thermal Comfort Based on Machine Learning
Thermal comfort is an important indicator of indoor environment quality and affects human health.It is an important reference for intelligent building systems,air conditioning control,and other systems.Moreover,it can effectively reduce the energy demand for controlling the thermal environment in buildings.Currently,wearable de-vices such as smart watches and flexible sensors are extensively used to compile comprehensive data on human health.However,due to individual differences,physiological thermal responses to identical thermal conditions vary,and it is difficult to effectively predict the group thermal state for a personal thermal comfort model.Considering the limita-tions of the relatively small sample sizes and complex model deployments in previous studies,this work established an artificial climate chamber with environmental sensors and wearable devices to collect thermal comfort data of 60 subjects and leveraged machine learning to realize human thermal comfort modeling and prediction.Considering indi-vidual differences such as height,weight,and gender,three machine-learning algorithms,i.e.,extreme gradient boosting(XGBoost),random forest,and support vector classifier(SVC),were used to obtain an enhanced predic-tive thermal state model based on human physiological parameters and to classify thermal comfort.The results showed that the skin-temperature normalization process and its gradient result in three states(cold discomfort,comfort,and thermal discomfort).This facilitates the SVC algorithm to find the optimal hyperplane in high-dimensional space and classify the features.Comparative analysis of the feature importance of the random forest model before and after skin-temperature normalization revealed that normalization reduces the influence of individual differences such as weight,height,and gender on the predictive effect of the model.Of the three machine learning algorithms,the accuracy of SVC on the test set and the area under the curve(AUC)values of the three thermal states were higher than those of XGBoost and random forest.Hence,SVC has the best classification effect and generalization capability.
wearablethermal comfortindividual differencemachine learningprediction model
万方数据
维普
