Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm

doi:10.1016/j.renene.2020.08.077

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	Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm
	Hu, Jianming 1; Heng, Jiani 2; Wen, Jiemei 1; Zhao, Weigang 3
	2020-12-01
发表期刊	RENEWABLE ENERGY
ISSN	0960-1481
卷号	162 页码:1208-1226
摘要	Wind energy has become a kind of attractive alternative energy in power generation field due to its nonpolluting and renewable properties. Wind speed forecasting acts an important role in programming and operation of power systems. However, achieving high precision wind speed forecasts is still consider as an arduous and challenging issue with the randomization and transient exist in wind speed time series. For this reason, this paper proposed two novel de-noising-reconstruction-based hybrid models which consist of novel signal decomposed methods, feature selection approaches and predictors based on quantile regression and optimization algorithm to achieve more accurate short term wind speed forecasting. The developed hybrid models firstly eliminate inherent noise from the wind speed sequences via decomposed method and subsequently construct the appropriate datasets for the forecasting engines by adopting the feature selection method; finally, establish the predictors for the forecasting task. To verify the effectiveness of proposed forecasting models, 1-h and 2-h wind speed data collected from Yumen, Gansu province of China mainland is used as case studies. The computational results demonstrated that the developed hybrid models yield better performance contrast with those of other models involved in this research in terms of both wind speed deterministic and probabilistic forecasting. (c) 2020 Elsevier Ltd. All rights reserved.
关键词	Renewable energy Complete empirical mode decomposition with adaptive noise Quantile regression neural network Wind speed forecasting Distance correlation
DOI	10.1016/j.renene.2020.08.077
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[71701053] ; National Natural Science Foundation of China[71601020] ; Natural Science Foundation of Guangdong Province[Grant2020A151501527] ; Guangzhou University Research Fund[220030401] ; Guangzhou University Research Fund[6962091190]
WOS研究方向	Science & Technology - Other Topics ; Energy & Fuels
WOS类目	Green & Sustainable Science & Technology ; Energy & Fuels
WOS记录号	WOS:000590672700009
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/57799
专题	中国科学院数学与系统科学研究院
通讯作者	Heng, Jiani
作者单位	1.Guangzhou Univ, Coll Econ & Stat, Guangzhou, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Hu, Jianming,Heng, Jiani,Wen, Jiemei,et al. Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm[J]. RENEWABLE ENERGY,2020,162:1208-1226.
APA	Hu, Jianming,Heng, Jiani,Wen, Jiemei,&Zhao, Weigang.(2020).Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm.RENEWABLE ENERGY,162,1208-1226.
MLA	Hu, Jianming,et al."Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm".RENEWABLE ENERGY 162(2020):1208-1226.