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| A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting | |
Tang, L; Yu, LA; Wang, S; Li, JP ; Wang, SY
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| Source Publication | APPLIED ENERGY
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| Keyword | Nuclear Energy Consumption Forecasting Hybrid Ensemble Learning Paradigm Ensemble Empirical Mode Decomposition |
| Abstract | 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 | |
| Volume | 93Issue:1Pages:12,432-443 |
| ISSN | 0306-2619 |
| Subject Area | Chemical ; Energy & Fuels ; Engineering |
| Indexed By | SCI |
| Language | 英语 |
| WOS ID | WOS:000302836500051 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.casisd.cn/handle/190111/4229 |
| Collection | 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) |
| Recommended Citation 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. |
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