Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

doi:10.1109/ACCESS.2020.3048382

IR > 理学院

	Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm
	Hao, Jie 1,2; Zhu, Changsheng1 ; Guo, Xiuting1
	2021
发表期刊	IEEE Access
ISSN	2169-3536
卷号	9 页码:6478-6487
摘要	Under the background of big data, the use of massive online data to improve the real-time characteristics and reliability of wind power prediction and to reduce the impact of wind farms on the power grid makes the power supply and demand balance important problems to solve. This paper provides a new solution for short-term wind power forecasting to address these problems. In this paper, an improved random forest short-term prediction model based on the hierarchical output power is proposed, and it is used to forecast the power output of a real wind farm located in Northwest China. First, a chi-square test is adopted to discretize the power data to divide the large-scale training data and remove abnormal data. The novelty of this study is the establishment of a classification model with the output wind power as the classification target and the use of Poisson re-sampling to replace the bootstrap method of the random forest, that is, to improve the training speed of the random forest algorithm. The results indicate that the proposed technique can estimate the output wind power with an MSE of 0.0232, and the comparison illustrates the effectiveness and superiority of the proposed method. © 2013 IEEE.
关键词	Decision trees Economics Electric power system interconnection Electric utilities Predictive analytics Random forests Statistical tests Weather forecasting Wind power Chi-square tests Classification models Random forest algorithm Real time characteristics Short term prediction Short-term forecasting Short-term wind power forecasting Wind power predictions
DOI	10.1109/ACCESS.2020.3048382
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000608213000001
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20210409807256
EI主题词	Electric power transmission networks
EI分类号	443 Meteorology - 615.8 Wind Power (Before 1993, use code 611 ) - 706.1 Electric Power Systems - 706.1.1 Electric Power Transmission - 922.2 Mathematical Statistics - 961 Systems Science - 971 Social Sciences
来源库	Compendex
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/147257
专题	理学院计算机与通信学院
通讯作者	Zhu, Changsheng
作者单位	1.Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Peoples R China; 2.Northwest Minzu Univ, Sch Elect Engn, Lanzhou 730030, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 GB/T 7714	Hao, Jie,Zhu, Changsheng,Guo, Xiuting. Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm[J]. IEEE Access,2021,9:6478-6487.
APA	Hao, Jie,Zhu, Changsheng,&Guo, Xiuting.(2021).Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm.IEEE Access,9,6478-6487.
MLA	Hao, Jie,et al."Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm".IEEE Access 9(2021):6478-6487.

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