Load Forecasting Method of Integrated Energy System Based on CNN-BiLSTM with Attention Mechanism

Abstract

Load forecasting of integrated energy system is an important part of economic dispatch and optimal operation of integrated energy system. In order to solve the user level load characteristics of integrated energy system with strong volatility and complex multi energy coupling, a user level load forecasting method of integrated energy system based on CNN-BiLSTM with attention mechanism is proposed in this paper. Firstly, Pearson correlation coefficient is used to analyze the time correlation and multi energy load correlation of user level load. Then, a user level load forecasting method of integrated energy system based on CBLA is proposed. Finally, taking the energy consumption data of the actual integrated energy system as an example, the prediction effect is analyzed. By comparing with other prediction methods, it proves that the proposed method can effectively improve the load forecasting accuracy.