A Reliable Short‐Term Power Load Forecasting Method Based on VMD‐IWOA‐LSTM Algorithm

To reduce the short‐term load forecasting ( STLF ) error of off‐line forecasting model, a VMD‐IWOA‐LSTM (VIL ) method for STLF is proposed. Firstly, variational mode decomposition ( VMD ) is used to decompose the historical power load signals. Then, the decomposed signals are reconstructed according to the similarity of Pearson correlation coefficient ( PCC) , and meteorological data are chosen for each reconstructed component based on the set PCC threshold. The long short‐term memory ( LSTM ) models are used to predict the corresponding components, and improved whale optimization algorithm ( IWOA ) is used to optimize the parameters in LSTM . Finally, the forecast results of each component are added together to get the final forecast result. The experimental results of power load data in a certain area show that the proposed method has the advantages of strong anti‐interference performance and high prediction accuracy compared with other methods, and has strong practicability. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.