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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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    1. AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
    2. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    3. Hong, Wei-Chiang, 2011. "Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm," Energy, Elsevier, vol. 36(9), pages 5568-5578.
    4. Moazzami, M. & Khodabakhshian, A. & Hooshmand, R., 2013. "A new hybrid day-ahead peak load forecasting method for Iran’s National Grid," Applied Energy, Elsevier, vol. 101(C), pages 489-501.
    5. Wang, Jianjun & Li, Li & Niu, Dongxiao & Tan, Zhongfu, 2012. "An annual load forecasting model based on support vector regression with differential evolution algorithm," Applied Energy, Elsevier, vol. 94(C), pages 65-70.
    6. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    7. Amjady, N. & Keynia, F., 2009. "Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm," Energy, Elsevier, vol. 34(1), pages 46-57.
    8. Deihimi, Ali & Showkati, Hemen, 2012. "Application of echo state networks in short-term electric load forecasting," Energy, Elsevier, vol. 39(1), pages 327-340.
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