Deep neural network model for multi-factor indoor thermal comfort index estimation: Taking a high-speed railway station in Chengdu as an example

摘要

With the improvement of human living condition, how to obtain a favorable thermal comfort indoor based on big data is a promising research field in city computing. The study examined the total 12 factors which have effect on indoor thermal comfort and developed a simulation model of a high-speed railway station in Chengdu with Energy Plus software. Deep Neural Network model (DNN) is proposed to examine the relationship between selected factors and thermal comfort. To investigate the performance of proposed DNN, Linear Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT) are also developed. The result indicated that DNN performs best in terms of RMSE and R2 among three models.