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Day ahead load forecasting of integrated energy system based on multi-model combination

The load of user level integrated energy system changes rapidly and it is difficult to predict accurately. Therefore, a day ahead load forecasting method of integrated energy system based on multi-model combination was proposed. Firstly, the long short-term memory (LSTM) network model, convolutional neural network (CNN) model and harmony search (HS) optimized light gradient boosting machine (LightGBM) model were established. Then, the inverse root mean square error method (IRMSE) was used to combine the forecasting results of the three models to obtain the final forecasting value. The effectiveness of the proposed method was verified by the actual data of an integrated energy system. The results show that the proposed method is superior to the single prediction model and the simple average combination model, and has the best prediction accuracy for electric, cooling and heat loads.