Short-Term Power Load Forecasting Method Based on Improved Sparrow Search Algorithm, Variational Mode Decomposition, and Bidirectional Long Short-Term Memory Neural Network

作者：Ming Wen, Бо Лю, Hao Zhong, Zongchao Yu, Changqing Chen, Xian Yang, Xueying Dai, Lisi Chen · 发表于：Energies · 年份：2024 · DOI：10.3390/en17215280 · 被引用次数：7 · 研究领域：Energy Load and Power Forecasting、Evaluation Methods in Various Fields、Machine Fault Diagnosis Techniques

A short-term power load forecasting method is proposed based on an improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), and Bidirectional Long Short Term Memory (BiLSTM) neural network. First, the SSA is optimized by combining Tent chaotic mapping, reverse learning, and dynamic step adjustment strategy, and the VMD mode number and penalty factor are optimized by ISSA. Secondly, the initial load sequence is decomposed into several Intrinsic Mode Function (IMF) components using ISSA-VMD. The effective modal components are screened by Wasserstein Distance (WD) between IMF and the original signal probability density. Then, the effective modal components are reconstructed by the Improved Multi-scale Fast Sample Entropy (IMFSE) algorithm. Finally, the extracted features and IMF were input into the ISSA-BiLSTM model as input vectors for prediction.