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Short-term Forecast of Multi-load of Electrical Heating and Cooling in Regional Integrated Energy System Based on Deep LSTM RNN

Integrated energy load forecasting is a prerequisite for integrated energy system (IES) planning and operation, therefore, accurate and rapid integrated energy load forecasting has important practical value. This paper first introduces the coupling and complementary relationship between different forms of energy in the integrated energy system, then the structure of LSTM network neural unit model is introduced, furthermore, a short-term multi-load forecasting method based on deep LSTM network is proposed, which method includes the construction of deep neural network model, the preprocessing of load and weather input, the evaluation index of root mean squared error(RMSE) and the selection of optimal global parameters based on random search method. Finally, actual data is applied to verify the effectiveness of the proposed method. After the comparison with other load forecasting method, The deep LSTM network multi-load prediction method proposed can obtain more accurate results and is suitable for practical engineering applications