Prediction of District Heating Load Based on Grey Neural Network Model

Abstract

In this paper, the gray correlation analysis method is used to evaluate the factors affecting the district heating load, and the gray prediction is combined with the BP neural network algorithm to establish a gray neural network structure, which can screen the factors affecting the heating load and predict the heating load. The heating load forecasting and verification of a district heating load is carried out. By comparing the prediction results and errors of the gray correlation GM(1, N) model, it is shown that the gray neural network model can select the appropriate influencing factors and exclude impact factors with low relevance in the heat load forecasting, improve the accuracy of heat load forecasting, and provide a theoretical basis for regional heat load forecasting.