郑州大学学报（工学版）2025，Vol.46Issue(3)：50-58,9.DOI:10.13705/j.issn.1671-6833.2025.03.020
基于组合深度学习的风电功率区间预测
Wind Power Interval Prediction Based on Combined Deep Learning
摘要
Abstract
To improve the accuracy of wind power interval prediction,in this study,a combined deep learning-based wind power interval prediction model was proposed.Firstly,to address the imbalance between global optimi-zation ability and local exploration in the traditional dung beetle optimization(DBO)algorithm,an improved ver-sion POTDBO was introduced.This algorithm enhanced the global search capability and improved the local search strategy.By optimizing the decomposition number K and penalty factor β in the variational mode decomposition(VMD),thus it improved the decomposition performance of VMD.Secondly,based on the optimized VMD decom-position results,a combined deep learning model,POTDBO-VMD-CNN-BiLSTM,was established.In this model,convolutional neural networks(CNN)were used to extract the spatial features of wind power,and a bidirectional long short-term memory(BiLSTM)network was applied to capture both historical and future signal features in the data.The individual components were predicted and then combined to reconstruct the wind power prediction accu-rately.To perform interval prediction for wind power,in this study the non-parametric kernel density estimation(KDE)method was introduced to fit the prediction errors of the combined model,then to obtain wind power inter-val prediction results at different confidence levels.Finally,the proposed model was validated using actual opera-tion data from a wind farm in Xinjiang.Simulation results showed that,at a 95%confidence level,compared to the Gaussian and T-distribution models,the proposed method reduced the prediction interval coverage width(CWC)by 0.103 6 and 0.171 4,respectively,while improving the interval prediction accuracy.关键词
风电功率区间预测/蜣螂优化算法/变分模态分解/非参数核密度估计Key words
wind power interval prediction/dung beetle optimization algorithm/variational mode decomposition/kernel density estimation分类
动力与电气工程引用本文复制引用
蒋建东,赵云飞,韩文轩,燕跃豪,鲍薇,刘晓辉..基于组合深度学习的风电功率区间预测[J].郑州大学学报（工学版）,2025,46(3):50-58,9.基金项目
河南省高等学校重点科研项目(24A470009) （24A470009）
