A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting

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• 作者：Ling Tang^a ; ^b ; Lean Yu^c ; ^d ; ^e ; ^{yulean@amss.ac.cn} ; Shuai Wang^a ; ^b ; Jianping Li^a ; Shouyang Wang^d ; ^e
• 关键词：Nuclear energy consumption forecasting ; Hybrid ensemble learning paradigm ; Ensemble empirical mode decomposition
• 刊名：Applied Energy
• 出版年：2012
• 期刊代码：2_03062619
• 类别：pw
• 出版时间：May, 2012
• 卷：93
• 期：Complete
• 页码：432-443
• 文件大小：808 K

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 鈥渄ecomposition and ensemble鈥? 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 IMFs 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.