现代电力2026，Vol.43Issue(2)：244-252,9.DOI:10.19725/j.cnki.1007-2322.2023.0431
耦合极点对称模态分解和小波包方法在短期风电出力预测中的应用
Application of Extreme-point Symmetric Mode Decomposition and Wavelet Packet Decomposition Method in Short-term Wind Power Prediction
摘要
Abstract
To mitigate the impact of uncertainty and volatility of wind power on the power grid,a gate recurrent unit neural network prediction model is proposed based on extreme-point symmetric mode decomposition and wavelet packet decomposition.Firstly,the original wind power output sequence is decomposed several times to thoroughly excavate the patterns among the sequences.Then,a gate recurrent unit neural network is employed to predict the decomposed sub-modes.Finally,the prediction results of the integrated model are compared with those of the gate recurrent unit neural network and BP neural network.Taking a wind farm in Altay,Xinjiang as an example,the validity of the model is verified.It has been demonstrated that the proposed model can substantially enhance the forecasting accuracy of short-term wind power generation.关键词
极点对称模态分解/门控循环单元神经网络/小波包分解/风电出力预测Key words
extreme-point symmetric mode decomposition/gate recurrent unit neural network/wavelet packet decomposition/wind power output forecast分类
能源科技引用本文复制引用
李斌,丁一,刘振路,包哲,李薇..耦合极点对称模态分解和小波包方法在短期风电出力预测中的应用[J].现代电力,2026,43(2):244-252,9.基金项目
国家自然科学基金项目(62073134).Project Supported by National Natural Science Foundation of China(62073134). （62073134）
