Research on central heating load prediction models based on machine learning
In order to ensure the heat demand of customers,improve the energy utilization rate,and accurately predict the heating load.Using customer complaint rate,outdoor weather conditions and historical load data as input features,four load prediction models are developed by using BP neural network,genetic algorithm optimized BP neural network(GA-BP),support vector regression machine(SVR)and long short-term memory network(LSTM)to predict the heating load for a future period on a time-by-time basis,and are trained and validated with actual heating data.The results show that compared with the other three prediction models,GA-BP has the highest prediction accuracy,the goodness-of-fit R2 can reach 0.994,and the minimum average absolute percentage error is 1.20%,which can be applied to the actual heating load prediction and regulation.
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