Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm | |
Hao, Jie1,2; 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 | |
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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