Study on Thermal Comfort Model Based on Genetic Algorithm with Backpropagation Neural Network

Abstract

To reduce fossil fuel energy consumption and improve the using efficiency, it is of great significance to study the thermal comfort model based on multiple physiological parameters. Compared with the classical models, comfort can be reflected more accurate by using the thermal comfort model based on multiple physiological parameters. In this paper, the experiments were performed to verify the effectiveness of the thermal comfort model. In particular, to verify the practicability of the thermal comfort model based on multiple physiological parameters, the established thermal comfort model based on the genetic algorithm with a backpropagation neural network and the classical PMV were compared. The results indicate that the established thermal comfort model is reasonable, which provides a feasible option for achieving a comfortable indoor environment. Finally, it puts forward further study on the thermal comfort model based on more physiological parameters.