LSTM Model for Various Types of Load Forecasting in Energy System Integration

Increasing energy efficiency and reducing pollution to the environment, a comprehensive energy system is of great significance for building an energy internet and promoting the transformation of the energy structure. Energy demand forecasting plays an important role in optimizing comprehensive energy system planning, and its accuracy is of great significance for maintaining stability and economic operation of the system. A load forecasting method based on Long Short-Term Memory Network Model (LSTM) is proposed in this paper. Firstly, calculating the mutual information load value of the past 1 hour time to the past 168 hour times, and the time to be predicted, and then using the maximum correlation and minimum redundancy to filter the input variables. Finally, on the basis of selecting the optimal input variable set, the multi-element load forecasting model of integrated energy system based on long-short-term memory neural network (LSTM) is established to realize the load data forecasting of the regional integrated energy system.