Prediction of district heating load based on grey neural network model

Evaluates the influence factors of district heating load by grey relativity analysis, and establishes a structure of grey neural network by combining grey prediction with BP neural network algorithm, which can screen the factors affecting the heating load and predict the heating load. Predicts and verifies the heating load of a district heating system. By comparing and screening the prediction results and errors of grey neural networks with different influence factors, the results show that the grey neural network model can select the appropriate influence factors and exclude the influence factors with low relativity degree in heating load prediction, improve the accuracy of heating load prediction, and provide a theoretical basis for district heating load prediction.