A Short-term Power Load Forecasting Method Based on An Improved VMD-LSTM Algorithm

Abstract

Accurate power load prediction is a crucial basis to ensure the balance of power supply and demand. This paper provides a modified variational modal decomposition (VMD) - long short-term memory (LSTM) algorithm, using genetic algorithm (GA) to iteratively optimize the number of decomposition layers and penalty factor parameters, and using VMD to obtain the modal components, fuse them with the filtered features. Then the reconstruction data is obtained through normalization according to the parameters of the original load characteristic data set. Finally, LSTM is constructed to excavate temporal characteristics and obtain power load prediction results. Simulation fully demonstrates the superior performance of this modified model in terms of prediction effect.