Journal
ENERGY
Volume 36, Issue 11, Pages 6542-6554Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2011.09.010
Keywords
Hydropower consumption forecasting; LSSVR ensemble Learning; Seasonal decomposition
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Funding
- National Science Fund for Distinguished Young Scholars [71025005]
- National Natural Science Foundation of China (NSFC) [90924024, 70601029]
- Chinese Academy of Sciences
- K.C. Wong Education Foundation, Hong Kong
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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.
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