Application of neural network to building environmental prediction and control

Abstract

Energy conservation, environmental protection, and intelligence are topics of interest in intelligent buildings. However, the energy requirement of various electrical equipment in intelligent buildings increases energy consumption. This study presents a neural network-based prediction and control system for the regulation of building environmental parameters. Neural network-based soft sensing technology can detect building environmental parameters through few sensors. The proposed system control algorithm can realize the adaptive adjustment of environmental parameters by using a neural network proportional–integral–derivative controller. Zigbee wireless communication is adopted as the information transmission medium to realize the environmental parameter measurement and network control. The soft sensing technique combined with Zigbee communication technology can effectively reduce energy consumption. The central control system analyzes the data coming from the network and regulates the environmental parameter through lifting temperature, ventilation, and switching curtains by using the neural network proportional–integral–derivative algorithm. The regulation of environmental parameters reduces unnecessary energy consumption. Finally, the effectiveness of the system is verified through simulations. Practical applications: This work reports an energy saving scheme. The building communication system constructed by ZigBee can reduce energy consumption and can be easily expanded. The soft sensing technique based on artificial neural network can predict temperatures by using few sensors. The neural network proportional–integral–derivative control algorithm has good performance in the regulation of environmental parameters for a time-varying system. Building energy consumption can be reduced by conducting these measures.