Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks

doi:10.1016/j.energy.2021.121981

CSpace > 应用数学研究所

	Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks
	Li, Dan1,2 ; Jiang, Fuxin1,2 ; Chen, Min1,2,3 ; Qian, Tao 3
	2022
发表期刊	ENERGY
ISSN	0360-5442
卷号	238 页码:22
摘要	Recently, the boom in wind power industry has called for the accurate and stable wind speed forecasting, on which reliable wind power generation systems depend heavily. Due to the intermittency and complexity of wind, an appropriate decomposition is proved as a pivotal part in the precise wind speed prediction. On this account, this paper constructs a hybrid decomposition method coupling the ensemble patch transform (EPT) and the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), where EPT is utilized to extract the trend of wind speed, then CEEMDAN is employed to divide the volatility into several fluctuation components with different frequency characteristics. Subsequently, the proposed decomposition method is combined with temporal convolutional networks (TCN) for the individual prediction of the trend and fluctuation components. Ultimately, the forecasted values for the wind speed prediction are obtained by reconstructing the prediction results of all the components. To evaluate the performance of the proposed EPT-CEEMDAN-TCN model, the historical wind speed data from three wind farms across China are used. The experimental results verify the notable effectiveness and necessity of the proposed EPT-CEEMDAN decomposition. In the meanwhile, the results demonstrate the significant superiority of the proposed EPT-CEEMDAN-TCN model on accuracy and stability. (c) 2021 Elsevier Ltd. All rights reserved.
关键词	Wind speed forecasting Ensemble patch transform Complete ensemble empirical mode decomposition Temporal convolutional network Hybrid method
DOI	10.1016/j.energy.2021.121981
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China, China[11690014] ; National Natural Science Foundation of China, China[11731015] ; Science and Technology Development Fund, Macau SAR[0123/2018/A3]
WOS研究方向	Thermodynamics ; Energy & Fuels
WOS类目	Thermodynamics ; Energy & Fuels
WOS记录号	WOS:000702790700005
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/59354
专题	应用数学研究所
通讯作者	Chen, Min
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 3.Macau Univ Sci & Technol, Macao Ctr Math Sci, Macau 999078, Peoples R China
推荐引用方式 GB/T 7714	Li, Dan,Jiang, Fuxin,Chen, Min,et al. Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks[J]. ENERGY,2022,238:22.
APA	Li, Dan,Jiang, Fuxin,Chen, Min,&Qian, Tao.(2022).Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks.ENERGY,238,22.
MLA	Li, Dan,et al."Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks".ENERGY 238(2022):22.