Short-term power load forecasting model based on VMD-SSA-LSTM

Accurate short-term load forecasting is crucial for the economic operation of power grids. In order to improve the accuracy of power system load forecasting, this paper proposes a sparrow optimization algorithm (SSA) to optimize the long short-term memory (LSTM) algorithm, taking into account the nonlinearity of the grid load and the characteristics of the many factors affecting the grid load, and combining with the variational modal decomposition (VMD) based on the proposed algorithm, so as to improve the accuracy of load forecasting. The experiment adopts the actual electricity load data of a certain region, and after comparing and analyzing with different benchmark models, it is verified that the proposed model can improve the prediction accuracy and has certain application potential.