Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation energy management systems. The dynamic balance between generation load the optimization of systems receiving increasing attention. intellectual development information industry data acquisition system smart grid provides a vast source for pessimistic forecasting, it great significance mining behind data. An accurate short-term can guarantee system’s safe reliable operation, improve utilization rate generation, avoid waste resources. In this paper, model by applying fusion Improved Sparrow Search Algorithm Long Short-Term Memory Neural Network (ILSTM-NN), then establish using novel model. swarm intelligence algorithm that simulates sparrow foraging predatory behavior. It used to optimize parameters (such as weight, bias, etc.) ILSTM-NN. results actual examples are prove accuracy forecasting. (decrease) MAPE about 20% 50% RMSE 44.1% 52.1%. Its ability error values tremendous, so very suitable promoting domestic system.