Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting

doi:10.1016/j.enconman.2020.113500

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	Causal convolutional gated recurrent unit network with multiple decomposition methods for short-term wind speed forecasting
	Zhang, Guowei 1,2; Liu, Da 3
	2020-12-15
发表期刊	ENERGY CONVERSION AND MANAGEMENT
ISSN	0196-8904
卷号	226 页码:15
摘要	Wind 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.
关键词	Causal convolutional network Gated recurrent unit Multiple decomposition methods Short-term wind speed forecasting
DOI	10.1016/j.enconman.2020.113500
收录类别	SCI
语种	英语
WOS研究方向	Thermodynamics ; Energy & Fuels ; Mechanics
WOS类目	Thermodynamics ; Energy & Fuels ; Mechanics
WOS记录号	WOS:000603338200011
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/57929
专题	中国科学院数学与系统科学研究院
通讯作者	Liu, Da
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.North China Elect Power Univ, Sch Econ & Management, Beijing 102206, Peoples R China
推荐引用方式 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.