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Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting
Zhang, Guowei1,2; Liu, Da3
2020-12-15
Source PublicationENERGY CONVERSION AND MANAGEMENT
ISSN0196-8904
Volume226Pages:15
AbstractWind speed exhibits different and complex fluctuation characteristics, which makes it challenging for wind speed forecasting. Decomposition methods have been widely and successfully applied in wind speed forecasting, for they could extract the fluctuation patterns by decomposing wind speed into sub-signals. However, the sub-signals are always modeled and forecasted separately, which neglects the intercorrelations of the sub-signals. Capturing the intercorrelations helps to obtain more effective features and further improve the forecasting performance. To address this issue, we propose a new hybrid model by combining a causal convolutional network (CCN), a gated recurrent unit (GRU) network, and multiple decomposition methods. In the proposed model, multiple decomposition methods are adopted to decompose the original wind speed into diversified sub-signals, CCN is applied to extract more effective features from the decomposed sub-signals, and GRU is employed to identify the temporal dependencies between the extracted features and future wind speed. Four wind speed datasets collected in different seasons are introduced for experimental analysis. The experimental results demonstrate that: (1) the proposed model outperforms the benchmark models consistently in terms of forecasting accuracy and stability; (2) the forecasting performance of the proposed model could be significantly improved by using multiple decomposition methods; (3) CCN and GRU adopted in the proposed model are both effective for improving the forecasting performance.
KeywordCausal convolutional network Gated recurrent unit Multiple decomposition methods Short-term wind speed forecasting
DOI10.1016/j.enconman.2020.113500
Indexed BySCI
Language英语
WOS Research AreaThermodynamics ; Energy & Fuels ; Mechanics
WOS SubjectThermodynamics ; Energy & Fuels ; Mechanics
WOS IDWOS:000603338200011
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/57929
Collection中国科学院数学与系统科学研究院
Corresponding AuthorLiu, Da
Affiliation1.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.North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Guowei,Liu, Da. Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting[J]. ENERGY CONVERSION AND MANAGEMENT,2020,226:15.
APA Zhang, Guowei,&Liu, Da.(2020).Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting.ENERGY CONVERSION AND MANAGEMENT,226,15.
MLA Zhang, Guowei,et al."Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting".ENERGY CONVERSION AND MANAGEMENT 226(2020):15.
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