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A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014

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  • Juban, Romain
  • Ohlsson, Henrik
  • Maasoumy, Mehdi
  • Poirier, Louis
  • Kolter, J. Zico

Abstract

This paper proposes a generic framework for probabilistic energy forecasting, and discusses the application of the method to several tracks in the 2014 Global Energy Forecasting Competition (GEFCom2014). The proposed method uses a multiple quantile regression approach to predict a full distribution over possible future energy outcomes, uses the alternating direction method of multipliers to solve the optimization problems resulting from this quantile regression formulation efficiently, and uses a radial basis function network to capture the non-linear dependencies on the input data. For the GEFCom2014 competition, the approach proved general enough to obtain one of the top five ranks in three tracks, solar, wind, and price forecasting, and it was also ranked seventh in the final load forecasting track.

Suggested Citation

  • Juban, Romain & Ohlsson, Henrik & Maasoumy, Mehdi & Poirier, Louis & Kolter, J. Zico, 2016. "A multiple quantile regression approach to the wind, solar, and price tracks of GEFCom2014," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1094-1102.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1094-1102
    DOI: 10.1016/j.ijforecast.2015.12.002
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    References listed on IDEAS

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    1. Neocleous, Tereza & Portnoy, Stephen, 2008. "On monotonicity of regression quantile functions," Statistics & Probability Letters, Elsevier, vol. 78(10), pages 1226-1229, August.
    2. Hong, Tao & Pinson, Pierre & Fan, Shu & Zareipour, Hamidreza & Troccoli, Alberto & Hyndman, Rob J., 2016. "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond," International Journal of Forecasting, Elsevier, vol. 32(3), pages 896-913.
    3. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
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