Analysisand research on the heat load forecasting of solar central heating

Abstract

With the decrease of traditional energy reserves and the improvement of environmental protection requirements, people pay more and more attention to the efficient and clean application of new energy[1]. In this paper, through the analysis of the historical data of a heat exchange station in a small district in Lanzhou, two prediction algorithmsof BP neural network and SVM support vector product are used to predict the heat load. The two predictionalgorithmsareusedtopredicttheheatload, and the optimal prediction algorithm is found out based on the analysis results. On this basis, the optimal prediction algorithm is used to make long-term and short-term prediction of heat load. Accurate prediction of solar energy central heating system can provide reference for the operation of solar thermal storage system, make great use of the utilization rate of solar energy in the heating system, reduce the waste of clean energy in the solar central heating system, reduce the use cycle of traditional energy and reduce carbon emissions. At the same time, forecasting the heating load in advance can reduce the waste caused by the lag of hot water, reduce the energy consumption of the heating system, and enable the thermal company to provide heat according to the needs of users, reduce costs and improve customer satisfaction.