电力系统及其自动化学报2025，Vol.37Issue(4)：30-39,10.DOI:10.19635/j.cnki.csu-epsa.001508
基于优化VMD和BiLSTM的短期负荷预测
Short-term Load Forecasting Based on Optimized VMD and BiLSTM Network
摘要
Abstract
To solve the problems of strong periodicity,high volatility and poor prediction effect in power load data,an ensemble prediction model based on optimized variational mode decomposition(VMD),improved sand cat swarm opti-mization(ISCSO)algorithm and bidirectional long short-term memory(BiLSTM)network was established.First,the VMD of the original power load data was carried out to reduce the data complexity,in which the beluga whale optimiza-tion algorithm was introduced to optimize the number of decomposition layers and penalty factors to optimize the decom-position effect.Second,a multi-strategy improvement method introduced by Logistic chaotic mapping,spiral search and sparrow thought was used to increase the population diversity of the original sand cat swarm optimization algorithm and improve the convergence accuracy and global search capability,and the improved algorithm was used to optimize the hyperparameters in BiLSTM network.Third,combined with the AdaBoost ensemble learning algorithm,an ISCSO-BiLSTM-AdaBoost prediction model was constructed,and the decomposed components were input into the model for prediction.Finally,the predicted values were superimposed to obtain the final prediction result.Experimental results show that the combined model established in this paper has a high prediction accuracy and strong stability.关键词
电力负荷预测/变分模态分解/双向长短期记忆网络/改进沙猫群优化算法/集成学习算法Key words
power load prediction/variational mode decomposition(VMD)/bidirectional long short-term memory(BiLSTM)network/improved sand cat swarm optimization(ISCSO)algorithm/ensemble learning algorithm分类
信息技术与安全科学引用本文复制引用
谢国民,陆子俊..基于优化VMD和BiLSTM的短期负荷预测[J].电力系统及其自动化学报,2025,37(4):30-39,10.基金项目
国家自然科学基金资助项目(51974151) （51974151）
依托项目:辽宁省教育厅重点实验室基金资助项目(LJZS003). （LJZS003）
