河南理工大学学报（自然科学版）2026，Vol.45Issue(5)：11-19,9.DOI:10.16186/j.cnki.1673-9787.2025100053
基于二次分解协同TCNN-GRU的短期风电功率预测
Short-term wind power prediction based on quadratic decomposition cooperative TCNN-GRU
摘要
Abstract
Objectives In order to improve the accuracy of wind power prediction and ensure the dispatch and stable operation of the power grid,a short-term wind power prediction model based on quadratic de-composition cooperative TCNN-GRU was proposed.Methods First,a quadratic decomposition module,con-structed using complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and variational mode decomposition(VMD),was used to decompose the original wind signal and reduce its complexity.Then,all the decomposed components were extracted for features using a two-way convolutional neural network(TCNN)to explore the feature relationships between the components.Finally,the gated re-current unit(GRU)was introduced to model the time series of all wind power sub-components,and the prediction results were obtained through cumulative reorganization.Results Experiments were performed on measured data from wind farms with a sampling interval of 15 min.Compared with GA-BP,PSO-ELM and CNN-GRU,the TCNN-GRU model proposed in this paper performed optimally on datasets with different sample sizes.Ablation experiments on the model in this paper showed that,compared with the single de-composition,the RMSE and MAE values of the prediction results after the secondary decomposition de-creased by 16.49%and 5.22%,respectively,and the R2 improved by 0.02.Compared with the CEEMDAN-VMD-GRU that did not incorporate the two-way convolutional neural network,the RMSE and MAE values of its prediction results decreased by 44.59%and 62.17%,respectively,and the R2 improved by 0.03.In order to verify the applicability and accuracy of the model of this paper in different seasons,the annual data of wind farms were divided into four seasons for comparison experiments,and the error calculation re-sults showed that its performance in different seasons was optimal.Conclusions Comprehensive results of the above experiments showed that the method in this paper was reasonable in design,and the quadratic decomposition method and TCNN-GRU could effectively improve the prediction accuracy and perform well under different climatic conditions,which was highly practical.关键词
二次分解/双路卷积神经网络/门控循环单元/短期风电功率预测Key words
quadratic decomposition/convolutional neural network/gated recurrent unit/short-term wind power prediction分类
信息技术与安全科学引用本文复制引用
刘辉,陈文豪..基于二次分解协同TCNN-GRU的短期风电功率预测[J].河南理工大学学报（自然科学版）,2026,45(5):11-19,9.基金项目
国家自然科学基金资助项目(61903129) （61903129）
