系统仿真学报 ›› 2021, Vol. 33 ›› Issue (11): 2606-2614.doi: 10.16182/j.issn1004731x.joss.21-FZ0705

• 仿真建模理论与方法 • 上一篇    下一篇

基于随机森林的风电功率短期预测方法

刘兴, 王艳, 纪志成   

  1. 江南大学 教育部物联网技术应用工程中心,江苏 无锡 214122
  • 收稿日期:2021-04-17 修回日期:2021-07-15 出版日期:2021-11-18 发布日期:2021-11-17
  • 通讯作者: 王艳(1978-),女,博士,教授,研究方向为制造系统能效优化。E-mail:wangyan@jiangnan.edu.cn
  • 作者简介:刘兴(1995-),男,硕士生,研究方向为风电功率短期预测。E-mail:1549577492@qq.com
  • 基金资助:
    国家重点研发计划(2018YFB1701903); 国家自然科学基金(61973138)

Short-term Wind Power Prediction Method Based on Random Forest

Liu Xing, Wang Yan, Ji Zhicheng   

  1. Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China
  • Received:2021-04-17 Revised:2021-07-15 Online:2021-11-18 Published:2021-11-17

摘要: 为了对短期风电功率及其波动范围作出有效预测,提出了一种基于Kmeans聚类和核主成分分析法结合随机森林算法的风电功率预测方法。采用聚类分析数据处理方法对气象风力发电数据进行预处理,提高数据质量,使用核主成分分析法对风电数据的8个特征数据进行降维处理去除特征间的相关性,采用随机森林算法进行预测,得到风电功率的预测值。结果表明:与传统的预测模型相比,采用聚类和核主成分分析法结合随机森林算法的模型进行预测,降低了预测误差,并能更准确地跟踪风电功率的变化。

关键词: 组合预测, 聚类分析, 核主成分分析法, 随机森林算法, 风电功率预测

Abstract: In order to effectively predict the power and value fluctuation range of the short-term wind, a wind power prediction method based on clustering and kernel principal component analysis combined with random forest algorithm is proposed. The clustering analysis data processing method is used to preprocess the meteorological wind power generation data to improve the data quality, and the kernel principal component analysis method is used to reduce the dimensionality of the eight groups of characteristic data to remove the correlation of the wind power data, the random forest algorithm is used to forecast the wind power, to obtain the predicted wind power value. The results show that, compared with the traditional prediction model, based on the clustering and kernel principal component analysis, combined with the random forest algorithm, the prediction model can reduce the prediction error and track the change of wind power more accurately.

Key words: clustering analysis, combination forecast, kernel principal component analysis, random forest algorithm, wind power prediction

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