欢迎访问《太阳能学报》官方网站,今天是 分享到:
ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (3): 426-432.DOI: 10.19912/j.0254-0096.tynxb.2020-0524

• • 上一篇    下一篇

基于灰色关联分析的BSO-ELM-AdaBoost 风电功率短期预测

叶家豪1, 魏霞1, 黄德启1, 谢丽蓉1, 黄晨晨1, 赵世成2   

  1. 1.新疆大学电气工程学院,乌鲁木齐 830047;
    2.中建八局第一建设有限公司,济南 250000
  • 收稿日期:2020-06-08 发布日期:2022-09-28
  • 通讯作者: 魏 霞(1977—),女,硕士、副教授、硕士生导师,主要从事可再生能源方面的研究。30462111@qq.com
  • 基金资助:
    国家自然科学基金(51468062)

SHORT-TERM FORECAST OF WIND POWER BASED ON BSO-ELM-ADABOOST WITH GREY CORRELATION ANALYSIS

Ye Jiahao1, Wei Xia1, Huang Deqi1, Xie Lirong1, Huang Chenchen1, Zhao Shicheng2   

  1. 1. School of Electrical Engineering, Xinjiang University, Urumqi 830047, China;
    2. China Construction Eighth Engineering Bureau First Construction Co., Ltd., Ji’nan 250000, China
  • Received:2020-06-08 Published:2022-09-28

摘要: 提出基于灰色关联分析与自适应提升的天牛群优化极限学习机风电功率短期预测方法。首先,利用灰色关联分析构建训练样本集,提高历史数据与预测日时间尺度上的信息关联度。在此基础上,利用天牛群算法优化极限学习机,为极限学习机寻找最优权阈值,提高其泛化能力。最后,引入集成学习理念,通过自适应提升算法学习组合多个极限学习机弱预测器,对预测误差进行修正,实现误差权重的自分配与重组。以此构成的极限学习机强预测器可进一步提高模型的预测精度,结合西北某风电场实际数据验证该方法的有效性。

关键词: 风电, 功率预测, 自适应提升, 灰色关联分析, 天牛群算法, 极限学习机

Abstract: A novel method of short-term wind power prediction based on grey relational analysis and beetle swarm optimization extreme learning machine is proposed in this paper. Firstly, the gray correlation analysis is used to construct a training sample set to improve the correlation between historical data and forecasting information on the daily time scale. Furthermore, the beetle swarm optimization algorithm is used to optimize the extreme learning machine and find the optimal weight threshold for the extreme learning machine to improve its generalization ability. Finally, the concept of integrated learning is introduced, and multiple weak predictors of extreme learning machines are combined through adaptive enhancement algorithm learning to correct the prediction errors to realize the self-allocation and reorganization of error weights. The strong predictor of the extreme learning machine formed further improves the prediction accuracy of the model and the effectiveness of the method is verified by the actual data of a wind farm in Northwest China.

Key words: wind power, power forecast, Adaptive Boosting, grey correlation analysis, beetle swarm algorithm, extreme learning machine

中图分类号: