LIU H, CHEN W H ,et al. Short-term wind power prediction based on quadratic decomposition cooperative TCNN-GRU[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(5):11-19.

doi:10.16186/j.cnki.1673-9787.2025100033

Received:2025/10/17

Revised:2025/12/08

Published:2026-07-24

Short-term wind power prediction based on quadratic decomposition cooperative TCNN-GRU

Liu Hui, Chen Wenhao

Hubei Provincial Grid Intelligent Control and Equipment Engineering Technology Research Center，Hubei University of Technology，Wuhan 430068，Hubei， China

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 decomposition cooperative TCNN-GRU was proposed.  Methods First， a quadratic decomposition module， constructed 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 recurrent 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 decomposition， the RMSE and MAE values of the prediction results after the secondary decomposition decreased 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 results 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.

Key words:quadratic decomposition;convolutional neural network;gated recurrent unit;short-term wind power prediction