Intelligent control method of heating process including model prediction and climate compensation

This paper takes the intelligent optimization control of the heating process as the technological background. Not only analyzes the shortcomings of the existing control strategies, but alse proposes and establishes a model predictive control strategy based on RBF neural network. In this paper, the mechanism model is used instead of the actual building, and the RBF neural network model is used as the predictive model. Furthermore, the control strategy includes the prediction of future interference and the feedback of indoor real-time temperature. This paper realizes the improvement of the traditional algorithm, improves the control effect, and finally reduces the energy consumption in the heating process while improving the comfort of thermal users.