A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

CASISD OpenIR > 中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)

	A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
	Tang, L; Yu, LA; Wang, S; Li, JP; Wang, SY
发表期刊	APPLIED ENERGY
关键词	Nuclear Energy Consumption Forecasting Hybrid Ensemble Learning Paradigm Ensemble Empirical Mode Decomposition
摘要	In this paper, a novel hybrid ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and least squares support vector regression (LSSVR) is proposed for nuclear energy consumption forecasting, based on the principle of "decomposition and ensemble". This hybrid ensemble learning paradigm is formulated specifically to address difficulties in modeling nuclear energy consumption, which has inherently high volatility, complexity and irregularity. In the proposed hybrid ensemble learning paradigm, EEMD, as a competitive decomposition method, is first applied to decompose original data of nuclear energy consumption (i.e. a difficult task) into a number of independent intrinsic mode functions (IMFs) of original data (i.e. some relatively easy subtasks). Then LSSVR, as a powerful forecasting tool, is implemented to predict all extracted IMFs independently. Finally, these predicted IMEs are aggregated into an ensemble result as final prediction, using another LSSVR. For illustration and verification purposes, the proposed learning paradigm is used to predict nuclear energy consumption in China. Empirical results demonstrate that the novel hybrid ensemble learning paradigm can outperform some other popular forecasting models in both level prediction and directional forecasting, indicating that it is a promising tool to predict complex time series with high volatility and irregularity. (C) 2011 Elsevier Ltd. All rights reserved.
	2012
卷号	93 期号:1 页码:12,432-443
ISSN	0306-2619
学科领域	Chemical ; Energy & Fuels ; Engineering
收录类别	SCI
语种	英语
WOS记录号	WOS:000302836500051
引用统计	被引频次：100[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.casisd.cn/handle/190111/4229
专题	中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
推荐引用方式 GB/T 7714	Tang, L,Yu, LA,Wang, S,et al. A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting[J]. APPLIED ENERGY,2012,93(1):12,432-443.
APA	Tang, L,Yu, LA,Wang, S,Li, JP,&Wang, SY.(2012).A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting.APPLIED ENERGY,93(1),12,432-443.
MLA	Tang, L,et al."A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting".APPLIED ENERGY 93.1(2012):12,432-443.

条目包含的文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
A novel seasonal dec（913KB）			开放获取	--	浏览