风功率预测关键技术及其研究应用综述

王磊; 滕伟; 武鑫; 高青风; 柳亦兵

doi:10.19805/j.cnki.jcspe.2026.250174

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风功率预测关键技术及其研究应用综述

新能源与储能 | 更新时间：2026-02-12

- 风功率预测关键技术及其研究应用综述
- A Review of Key Technologies and Research Applications in Wind Power Prediction
- 动力工程学报 2026年46卷第2期页码：98-112
- 作者机构：
  
  华北电力大学电站能量传递转化与系统教育部重点实验室,北京,102206
- 作者简介：
  
  [ "王磊(1986—),男,河南确山人,博士,研究方向为风电机组故障,E-mail:hais1998@163.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(51775186)
- DOI：10.19805/j.cnki.jcspe.2026.250174
  中图分类号：
- 网络首发：2026-02-10，
  
  纸质出版：2026-02-10
- 稿件说明：
移动端阅览
王磊,滕伟,武鑫,高青风,柳亦兵. 风功率预测关键技术及其研究应用综述动力工程学报, 2026, 46(2): 98-112 https://doi.

org/10.19805/j.cnki.jcspe.2026.250174
王磊,滕伟,武鑫,高青风,柳亦兵. 风功率预测关键技术及其研究应用综述动力工程学报, 2026, 46(2): 98-112 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.250174.

org/10.19805/j.cnki.jcspe.2026.250174 DOI：

摘要

风电有显著的间歇性与随机性特征

其输出功率随风速变化剧烈波动

特别是在电网疏散、渗透和并网方面

给电力系统的管理带来许多挑战。风功率预测技术作为缓解风电不确定性的核心手段

对提升电网稳定性、降低弃风率、优化电力市场交易及推动风电可持续发展具有重要意义。系统地解析了风功率预测的类型划分、基本原理架构及主流方法

深入对比物理建模法、统计分析法、机器学习法、组合法等风功率预测方法的适用场景、优势、局限及评价指标体系。在此基础上

全面综述预测精度提升的关键技术路径

涵盖多源数据融合、深度学习算法优化、误差校正机制等前沿研究方向

并总结最新研究成果。最后展望了风功率预测技术的未来发展趋势

提出基于数字孪生、强化学习与气象耦合建模的创新解决方案。

Abstract

Wind power exhibits significant intermittency and stochastic characteristics

with its output power experiencing severe fluctuations due to wind speed variations. This poses substantial challenges for power system management

particularly in grid dispersion

penetration

and grid connection. As a core means to mitigate wind power uncertainty

wind power prediction technology plays a crucial role in enhancing grid stability

reducing wind curtailment rates

optimizing electricity market transactions

and promoting the sustainable development of wind power. This study systematically analyzed the classification

fundamental principles

and mainstream methodologies of wind power prediction

conducting an in-depth comparisons of the application scenarios

advantages

limitations

and evaluation index systems of various wind power prediction methods

such as physical modeling

statistical analysis

machine learning

and hybrid methods. Based on this

a comprehensively review was presented about the key technological pathways for improving prediction accuracy

covering cutting-edge research directions such as multi-source data fusion

deep learning algorithm optimization

and error correction mechanisms

and the latest research findings were also summarized. Finally

the prospect of the future development trends in wind power prediction technology was provided

proposing innovative solutions based on digital twins

reinforcement learning

and meteorological coupled modeling.

关键词

Keywords

references

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