影响因子：4.2
DOI码：10.1016/j.epsr.2025.112593
所属单位：College of Shipbuilding Engineering, Harbin Engineering University
发表刊物：Electric Power Systems Research
刊物所在地：Switzerland
项目来源：Key Research Project in Universities of Henan Province (No.24B480012, No.25A450004), Key Specialized
关键字：Short-term electric load; Variational modal decomposition (VMD); Whale optimization algorithm (WOA); Sparrow search algorithm (SSA); Convolutional neural network (CNN); Bi-directional long short-term memory neural network (BiLSTM); Uncertainty analysis
摘要：This study proposes an innovative short-term power load hybrid prediction model. The model deeply integrates the WOA-VMD data decomposition technology, multi-dimensional uncertainty analysis method, and Sparrow Search Algorithm (SSA) optimization strategy. First, the power load data and meteorological data are selected and decomposed by the WOA-VMD method. Then, the statistical techniques are used to conduct multi-dimensional analysis for each modal, i.e., time-features, domain features, and digital features are extracted. This not only enhances the two-way integration of feature information, but also classifies the modal components. Specifically, the non-periodic components are input into the SSA-CNN-BiLSTM model, and the periodic components are input into the CNN-BiLSTM model. Meanwhile, the SSA is used to complete the optimal search of model parameters. Furthermore, the influence of meteorological factors are further integrated by jointly put into the prediction model. Finally, the output results of each model are reconstructed. Experimental results show that compared with traditional models, the hybrid model can more accurately capture the load variation characteristics, effectively reduce the prediction error, and significantly improve the goodness of fit. It provides reliable technical support for power departments to scientifically plan the construction and transformation of distribution networks.
备注：Key Research Project in Universities of Henan Province (No.24B480012, No.25A450004), Key Specialized Research and Development Breakthrough Program in Henan Province (No.2421022 40051)
合写作者：杨新宇,华心心,范国峰,王永敬,Anantkumar J. Umbarkar
第一作者：彭丽玲
论文类型：期刊论文
通讯作者：洪维强
论文编号：112593
学科门类：工学
文献类型：J
卷号：254
ISSN号：0378-7796
是否译文：否
发表时间：2026
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/pii/S0378779625011800?via%3Dihub