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

文献类型：期刊论文

作者Li, Dan1,2; Jiang, Fuxin1,2; Chen, Min1,2,3; Qian, Tao3
刊名ENERGY
出版日期2022
卷号238页码:22
关键词Wind speed forecasting Ensemble patch transform Complete ensemble empirical mode decomposition Temporal convolutional network Hybrid method
ISSN号0360-5442
DOI10.1016/j.energy.2021.121981
英文摘要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.
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
语种英语
WOS记录号WOS:000702790700005
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/59353]  
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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