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

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 dy...

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Bibliographic Details
Published in:Energies (Basel) Vol. 17; no. 21; p. 5280
Main Authors: Wen, Ming, Liu, Bo, Zhong, Hao, Yu, Zongchao, Chen, Changqing, Yang, Xian, Dai, Xueying, Chen, Lisi
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2024
Subjects:
Accuracy
Algorithms
Analysis
BiLSTM
Distribution (Probability theory)
Forecasting
Fourier transforms
improved variational mode decomposition
load forecasting
Methods
Neural networks
Optimization
sparrow optimization algorithm
Time series
Wavelet transforms
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en17215280