Modeling and Simulation of Restorative Indoor Environment Based on Neural Network

Abstract

In the busy modern society, after experiencing fast-paced work, what people most desire is to have a relatively relaxed and comfortable indoor rest scene. At the same time, with the improvement of material living standards, people have higher and higher requirements for the office and indoor rest environment, and the indoor temperature environment is very important to create a sense of comfort. A good indoor somatosensory temperature environment can not only convey the degree of somatosensory comfort but also relax people's bodies, in which the air conditioning control system can restore the indoor temperature environment. Based on the abovementioned problems, this paper uses the neural network modeling method, based on the indoor comfort index SET∗ value and other factors that can affect the environmental temperature, aiming at the human body temperature comfort, constructs a BP neural network model with indoor environmental parameters as the input index, and uses the improved particle swarm optimization algorithm to optimize the model, so as to realize the real-time control of SET∗ value, and then analyzes the relationship between indoor environmental thermal factors and SET∗ value, The model is optimized through simulation experiments to improve the optimization degree of the model. The practice shows that the restorative indoor environment model based on improved BP neural network constructed in this paper can optimize the room parameters well, so as to improve indoor comfort and give people a warm accommodation environment.