电力系统保护与控制2026，Vol.54Issue(5)：24-33,10.DOI:10.19783/j.cnki.pspc.250635
基于多维数据融合和CNN-BiLSTM联合优化的超短期风电功率预测
Ultra-short-term wind power forecasting based on multidimensional data fusion and joint optimization of CNN-BiLSTM
摘要
Abstract
Accurate wind power forecasting is an effective means of improving the stability of wind power grid integration and the economic performance of wind farms.Aiming at the prominent challenge that the complexity and randomness of natural meteorological characteristics make wind power difficult to predict accurately,this paper proposes an ultra-short-term wind power forecasting method based on multidimensional data fusion and the joint optimization of a convolutional neural network-bidirectional long short-term memory network(CNN-BiLSTM).The method consists of two main phases.First,in the input data processing phase,a new multidimensional feature data is constructed to improve the accuracy of the prediction model,which combines the key meteorological factors selected by principal component analysis(PCA)with the wind power intrinsic modal components obtained via optimized variational mode decomposition(OVMD).Second,in the joint optimization phase of the forecasting model,a cascaded hybrid forecasting model integrating CNN and BiLSTM is constructed,and the red-billed blue magpie optimizer(RBMO)is employed to jointly optimize the CNN and BiLSTM models.This allows the complementary advantages of the two models to be fully exploited,further enhancing forecasting accuracy.Comparative analyses of wind power forecasting results demonstrate that the proposed PCA-OVMD-RBMO-(CNN-BiLSTM)method achieves higher prediction accuracy than other benchmark forecasting methods.关键词
风电功率预测/主成分分析/最优变分模态分解/卷积神经网络/双向长短期神经网络Key words
wind power forecasting/principal component analysis/optimal variational mode decomposition/convolutional neural network/bidirectional long short-term memory network引用本文复制引用
马艺玮,刘智强,邹密,陈俊生,严冬..基于多维数据融合和CNN-BiLSTM联合优化的超短期风电功率预测[J].电力系统保护与控制,2026,54(5):24-33,10.基金项目
This work is supported by the National Natural Science Foundation of China(No.61703068). 国家自然科学基金项目资助(61703068) （No.61703068）
重庆市教育委员会科学技术研究项目资助(KJQN202504202) （KJQN202504202）
重庆市研究生科研创新项目资助(CYS23468,CYS23469) （CYS23468,CYS23469）
