Research and Analysis of Nonlinear Model Identification Control Algorithm Based on Improved Neural RBF For Short Term Heat Load Forecasting of Heat Supply Network

Abstract

Aiming at the mismatch between heat supply and demand of heating system, a nonlinear model identification control algorithm based on improved neural network for short-term heat load prediction of heat supply network is proposed by using the characteristics that heat load and temperature of heating system will not change dramatically in a short period of time By using MATLAB simulation, short-term heat load rolling prediction is realized. From the experimental results, this algorithm is better than the traditional RBF neural network in the prediction accuracy, and can accurately predict the trend of heat load.