A new dynamic integrated approach for wind speed forecasting

doi:10.1016/j.apenergy.2017.04.008

CSpace > 系统科学研究所

	A new dynamic integrated approach for wind speed forecasting
	Sun, Shaolong 1; Qiao, Han 2,3; Wei, Yunjie1,4,5 ; Wang, Shouyang1,2,5
	2017-07-01
发表期刊	APPLIED ENERGY
ISSN	0306-2619
卷号	197 页码:151-162
摘要	Wind energy is considered as one of the most promising and economical renewable energy. In order to insure maximum yield of wind energy, it is vital to evaluate wind energy potential of the wind farms. Since wind energy is proportional to the cube of wind speed, the evaluation of wind energy potential assessment comes down to the wind speed forecasting. In this paper, the wind speed is predicted by utilizing a new dynamic integrated approach. The novelties of this method mainly include: firstly, the Phase Space Reconstruction (PSR) is employed to dynamically choose the input vectors of the forecasting model; secondly, the data preprocessing approach, named the Kernel Principal Component Analysis (KPCA), is proposed to efficiently extract the nonlinear characteristics of the high-dimensional feature space reconstructed by the PSR; thirdly, Core Vector Regression(CVR) model, whose parameters are determined by the Competition Over Resource (COR) heuristic algorithm, is adopted to the model for quick computational speed; finally, the Grey Relational Analysis, Diebold-Mariano and PesaranTimmermann statistic are treated as evaluation tools to assess the forecasting effectiveness of this approach. The empirical results show that this integrated approach can significantly improve forecasting effectiveness and statistically outperform some other benchmark methods in terms of the directional forecasting and level forecasting. (C) 2017 Elsevier Ltd. All rights reserved.
关键词	Wind speed forecasting Core vector machine Phase space reconstruction Kernel principal component analysis Competition over resource algorithm
DOI	10.1016/j.apenergy.2017.04.008
语种	英语
资助项目	National Natural Science Foundation of China[71373262]
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:000401594300012
出版者	ELSEVIER SCI 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/25364
专题	系统科学研究所
通讯作者	Wang, Shouyang
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China 3.Univ Penn, Wharton Sch, Philadelphia, PA 19104 USA 4.City Univ Hong Kong, Dept Management Sci, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China 5.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Sun, Shaolong,Qiao, Han,Wei, Yunjie,et al. A new dynamic integrated approach for wind speed forecasting[J]. APPLIED ENERGY,2017,197:151-162.
APA	Sun, Shaolong,Qiao, Han,Wei, Yunjie,&Wang, Shouyang.(2017).A new dynamic integrated approach for wind speed forecasting.APPLIED ENERGY,197,151-162.
MLA	Sun, Shaolong,et al."A new dynamic integrated approach for wind speed forecasting".APPLIED ENERGY 197(2017):151-162.