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An ensemble approach for short-term load forecasting by extreme learning machine

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  • Li, Song
  • Goel, Lalit
  • Wang, Peng

Abstract

This paper proposes a novel ensemble method for short-term load forecasting based on wavelet transform, extreme learning machine (ELM) and partial least squares regression. In order to improve forecasting performance, a wavelet-based ensemble strategy is introduced into the forecasting model. The individual forecasters are derived from different combinations of mother wavelet and number of decomposition levels. For each sub-component from the wavelet decomposition, a parallel model consisting of 24 ELMs is invoked to predict the hourly load of the next day. The individual forecasts are then combined to form the ensemble forecast using the partial least squares regression method. Numerical results show that the proposed method can significantly improve forecasting performance.

Suggested Citation

  • Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    • Handle: RePEc:eee:appene:v:170:y:2016:i:c:p:22-29
    DOI: 10.1016/j.apenergy.2016.02.114
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    References listed on IDEAS

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