EN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
- 现代电力 2026年43卷第2期 页码：244-252
- 作者机构：
1. 国投电力控股股份有限公司, 北京市 西城区,100034
2. 资源环境系统优化教育部重点实验室(华北电力大学),北京市 昌平区,102206
- 作者简介：
[ "李斌(1969)，男，学士，高级工程师，研究方向为火电厂运行优化及大气污染物控制，E-mail：10000396@sdic.com.cn" ]
[ "丁一(1991)，男，硕士研究生，研究方向为电力系统建模仿真，E-mail：dingyi@sdic.com.cn" ]
[ "刘振路(1998)，男，硕士研究生，通信作者，研究方向为新能源出力预测，E-mail：liuzhenlu1213@163.com" ]
[ "包哲(1988)，男，博士研究生，研究方向为虚拟电厂建模仿真及运行优化，E-mail：baozhe@ncepu.edu.cn" ]
[ "李薇(1974)，女，博士，教授，研究方向为节能减排优化、能源与环境系统分析，E-mail：925657837@qq.com" ]
- 基金信息：国家自然科学基金项目(62073134)。
DOI：10.19725/j.cnki.1007-2322.2023.0431
中图分类号： 2-
纸质出版：2026
- 稿件说明：
移动端阅览
李斌, 丁一, 刘振路, 等. 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.
LI Bin, DING Yi, LIU Zhenlu, et al. Application of Extreme-point Symmetric Mode Decomposition and Wavelet Packet Decomposition Method in Short-term Wind Power Prediction[J]. 2026, 43(2): 244-252.
李斌, 丁一, 刘振路, 等. 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. DOI： 10.19725/j.cnki.1007-2322.2023.0431.
LI Bin, DING Yi, LIU Zhenlu, et al. Application of Extreme-point Symmetric Mode Decomposition and Wavelet Packet Decomposition Method in Short-term Wind Power Prediction[J]. 2026, 43(2): 244-252. DOI： 10.19725/j.cnki.1007-2322.2023.0431.
摘要
为降低风力发电的不确定性和波动性对电网造成的影响，提出一种耦合极点对称模态分解和小波包分解的门控循环单元神经网络预测模型。首先，对原始风电出力序列进行二次分解重构，充分挖掘出序列中的规律；然后，针对分解的子模态，使用门控循环单元神经网络进行预测；最后，将组合模型的预测结果与原始门控循环单元神经网络预测结果、反向传播神经网络预测结果进行对比。以新疆阿勒泰某风电场为算例，验证了该混合模态分解模型的有效性，结果表明所提模型可以显著提高短期风力发电的预测精度。
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
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references
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