A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China

doi:10.1016/j.energy.2011.09.010

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	A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China
	Wang, Shuai 2,4; Yu, Lean 1,3; Tang, Ling 2,4; Wang, Shouyang1,3
刊名	ENERGY
	2011-11-01
卷号	36 期号:11 页码:6542-6554
关键词	Hydropower consumption forecasting LSSVR ensemble Learning Seasonal decomposition
ISSN号	0360-5442
DOI	10.1016/j.energy.2011.09.010
英文摘要	Due to the distinct seasonal characteristics of hydropower, this study tries to propose a seasonal decomposition (SD) based least squares support vector regression (LSSVR) ensemble learning model for Chinese hydropower consumption forecasting. In the formulation of ensemble learning model, the original hydropower consumption series are first decomposed into trend cycle, seasonal factor and irregular component. Then the LSSVR with the radial basis function (RBF) kernel is used to predict the three different components independently. Finally, these prediction results of the three components are combined with another LSSVR to formulate an ensemble result for the original hydropower consumption series. In terms of error measurements and statistic test on the forecasting performance, the proposed approach outperforms all the other benchmark methods listed in this study in both level accuracy and directional accuracy. Experimental results reveal that the proposed SD-based LSSVR ensemble learning paradigm is a very promising approach for complex time series forecasting with seasonality. (C) 2011 Elsevier Ltd. All rights reserved.
资助项目	National Science Fund for Distinguished Young Scholars[71025005] ; National Natural Science Foundation of China (NSFC)[90924024] ; National Natural Science Foundation of China (NSFC)[70601029] ; Chinese Academy of Sciences ; K.C. Wong Education Foundation, Hong Kong
WOS研究方向	Thermodynamics ; Energy & Fuels
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000297894500028
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/11362]
专题	系统科学研究所
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Policy & Management, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, MADIS, Inst Syst Sci, Beijing 100190, Peoples R China 4.Chinese Acad Sci, Grad Univ, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Wang, Shuai,Yu, Lean,Tang, Ling,et al. A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China[J]. ENERGY,2011,36(11):6542-6554.
APA	Wang, Shuai,Yu, Lean,Tang, Ling,&Wang, Shouyang.(2011).A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China.ENERGY,36(11),6542-6554.
MLA	Wang, Shuai,et al."A novel seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China".ENERGY 36.11(2011):6542-6554.