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Short-term load forecasting using a kernel-based support vector regression combination model

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  • Che, JinXing
  • Wang, JianZhou

Abstract

Kernel-based methods, such as support vector regression (SVR), have demonstrated satisfactory performance in short-term load forecasting (STLF) application. However, the good performance of kernel-based method depends on the selection of an appropriate kernel function that fits the learning target, unsuitable kernel function or hyper-parameters setting may lead to significantly poor performance. To get the optimal kernel function of STLF problem, this paper proposes a kernel-based SVR combination model by using a novel individual model selection algorithm. Moreover, the proposed combination model provides a new way to kernel function selection of SVR model. The performance and electric load forecast accuracy of the proposed model are assessed by means of real data from the Australia and California Power Grid, respectively. The simulation results from numerical tables and figures show that the proposed combination model increases electric load forecasting accuracy compared to the best individual kernel-based SVR model.

Suggested Citation

  • Che, JinXing & Wang, JianZhou, 2014. "Short-term load forecasting using a kernel-based support vector regression combination model," Applied Energy, Elsevier, vol. 132(C), pages 602-609.
    • Handle: RePEc:eee:appene:v:132:y:2014:i:c:p:602-609
    DOI: 10.1016/j.apenergy.2014.07.064
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    References listed on IDEAS

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