A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting
A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting
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DOI：
10.1007/s10479-014-1595-5
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发表时间：
2015-11
影响因子：
4.8
通讯作者：
L. Tang;Shuai Wang;Kaijian He;Shouyang Wang
中科院分区：
文献类型：
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作者：
L. Tang;Shuai Wang;Kaijian He;Shouyang Wang
提出了一种新的基于模态特征的核能源消费预测分解集成模型。我们的方法是基于“基于数据特征的建模”和“分解与集成”的原则。该模型改进了现有的分解集成学习技术（“分解和集成”），通过使用“基于数据特征的建模”来预测分解模式。首先利用包络经验模态分解将原始的核能消费数据分解为一系列相对简单的模态，降低了数据的复杂度。然后，对提取的模式进行深入分析，以捕获隐藏的数据特征。这些特征用于确定每种模式的适当预测模型。通过使用有效的集成工具（如最小二乘支持向量回归）将这些预测的分量组合起来，获得最终预测。为了说明和验证的目的，我们已经实现了所提出的模型预测中国的核能消费。我们的数值结果表明，新的方法显着优于所有考虑的基准。这表明它是预测核能消费等复杂和不规则数据的一个非常有前途的工具。
We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the decomposed modes. Ensemble empirical mode decomposition is first used to decompose the original nuclear energy consumption data into a series of comparatively simple modes, reducing the complexity of the data. Then, the extracted modes are thoroughly analyzed to capture hidden data characteristics. These characteristics are used to determine appropriate forecasting models for each mode. Final forecasts are obtained by combining these predicted components using an effective ensemble tool, such as least squares support vector regression. For illustration and verification purposes, we have implemented the proposed model to forecast nuclear energy consumption in China. Our numerical results demonstrate that the novel method significantly outperforms all considered benchmarks. This indicates that it is a very promising tool for forecasting complex and irregular data such as nuclear energy consumption.