基于改进变分模态分解与Informer组合模型的风电功率多步预测研究

郭晓鹏; 赵琪; 张国维

doi:10.19725/j.cnki.1007-2322.2023.0429

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基于改进变分模态分解与Informer组合模型的风电功率多步预测研究

新能源电力系统 | 更新时间：2026-02-02

- 基于改进变分模态分解与Informer组合模型的风电功率多步预测研究
- Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model
- 现代电力 2026年43卷第1期页码：20-29
- 作者机构：
  
  新能源电力与低碳发展研究北京市重点实验室, 北京市昌平区,102206
- 作者简介：
  
  [ "郭晓鹏(1979)，男，博士，副教授，主要研究方向为能源经济管理、能源经济预测，E-mail：13520328997@163.com" ]
  [ "赵琪(1999)，女，硕士研究生，通信作者，主要研究方向为能源经济预测，E-mail：m17852738413@163.com" ]
  [ "张国维(1988)，男，博士，讲师，主要研究方向为风电功率预测、能源经济管理，E-mail：gwzhang@ncepu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(青年科学基金项目) (72301102)。
- DOI：10.19725/j.cnki.1007-2322.2023.0429
  中图分类号： 1
- 纸质出版：2026
- 稿件说明：
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郭晓鹏, 赵琪, 张国维. 基于改进变分模态分解与Informer组合模型的风电功率多步预测研究[J]. 现代电力, 2026,43(1):20-29.

郭晓鹏, 赵琪, 张国维. Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model[J]. 2026, 43(1): 20-29.
郭晓鹏, 赵琪, 张国维. 基于改进变分模态分解与Informer组合模型的风电功率多步预测研究[J]. 现代电力, 2026,43(1):20-29. DOI： 10.19725/j.cnki.1007-2322.2023.0429.

郭晓鹏, 赵琪, 张国维. Multi-step Prediction of Wind Power Based on Improved Variational Modal Decomposition and Informer Hybrid Model[J]. 2026, 43(1): 20-29. DOI： 10.19725/j.cnki.1007-2322.2023.0429.

摘要

保证风电功率预测的准确性是提高风能利用效率、实现电力系统可持续发展的关键工作。因此，该文提出一种基于改进变分模态分解与Informer组合模型的风电功率多步预测模型。首先，采用随机森林模型对风速、风向、压强等原始气象因素进行筛选。其次，通过鹈鹕优化算法改进后的变分模态分解算法对风电功率信号进行分解，从而提高风电序列预测精准性。第三，基于Informer模型对风电功率进行多步预测。最后，通过与其他模型进行对比分析，验证该模型在风电功率多步预测中的优越性。算例结果表明，基于改进变分模态分解与Informer组合模型的风电功率多步预测模型具有良好的预测性能，可为风电功率的预测提供参考。

Abstract

The accurate predition of wind power is crucial for enhancing the efficiency of wind energy utilization and achieving sustainable development of the power system. In view of this

a multi-step wind power prediction model based on the improved variational modal decomposition (VMD) with Informer is proposed in this paper. Firstly

the original meteorological factors such as wind speed

wind direction

and pressure are filtered using the random forest model. Secondly

the wind power signal is decomposed by the pelican optimization algorithm-improved VMD algorithm to enhance the accuracy of wind power sequence prediction. Thirdly

multi-step wind power prediction is performed using the Informer model. Finally

the superiority of this model in multi-step wind power prediction is verified through multi-dimensional comparison with other models. The case results demonstrate that the wind power multi-step prediction model based on the improved VMD with Informer exhibits excellent prediction performance and can provide reference for wind power prediction.

关键词

Keywords

references

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