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. (c) 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.