Comparative Study on Neural Network Prediction Models for Heat Load of Heating Station
Taking a heating station in Zheng-zhou as the research object,the influencing factors of heat load were determined by correlation analysis.The BP neural network prediction model,the RBF neural network prediction model and the BP neural network prediction model optimized by genetic algorithm were established respectively,and the prediction effects of the three prediction models were evaluated.The varia-tion trends of the predicted heat load of the three pre-diction models are basically consistent with those of the measured heat load,and the time series characteristics of the heat load can be objectively reflected.Compared with the BP prediction model and RBF prediction mod-el,the prediction value of the BP-GA prediction model is closer to the measured value,and the error and rela-tive error of the prediction value are smaller.Among the three prediction models,the BP-GA prediction model has the best prediction effect and the shortest training time.
万方数据
维普
