船电技术2026，Vol.46Issue(9)：6-9,4.
基于VMD-LSTM神经网络的风电功率预测研究
Research on Wind Power Prediction Based on VMD-LSTM Neural Network
摘要
Abstract
To better predict wind power,this paper proposes a wind power prediction method that combines Variational Mode Decomposition(VMD)with Long Short-Term Memory Network(LSTM).Firstly,VMD is used to decompose original wind power data,obtaining a series of relatively smooth intrinsic mode functions to simplify wind power data;then these data are used as the input of the LSTM model,and by leveraging the special gating mechanism of LSTM,the problems of gradient vanishing or gradient explosion are solved;finally,the predicted values corresponding to each intrinsic mode function are weighted and summed to obtain the final prediction result.In the simulation verification of a real wind farm,compared with individual LSTM and BP network models,the VMD-LSTM combined model proposed in this papwer has lower root mean square error(RMSE),average absolute error(MAE),and average absolute percentage error(MAPE),and thus improves the accuracy and robustness of wind power prediction,and provides more reliable technical support for power grid dispatching.关键词
风电功率预测/变分模态分解/长短期记忆网络Key words
Wind power prediction/Variational Mode Decomposition/Long Short-Term Memory Network分类
信息技术与安全科学引用本文复制引用
耿运涛,于培龙..基于VMD-LSTM神经网络的风电功率预测研究[J].船电技术,2026,46(9):6-9,4.基金项目
2025年度邵阳市科技计划指导性项目《基于LSTM神经网络的风电功率预测模型研究》(项目编号:2025GZ6090) （项目编号:2025GZ6090）
