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

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

International Journal of Forecasting, 2016, vol. 32, issue 3, 1094-1102

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.

Keywords: Probabilistic forecasting; Quantile regression; Alternating direction method of multipliers; Solar forecasting; Wind forecasting; Load forecasting; Price forecasting; GEFCom2014 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (29)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:1094-1102

DOI: 10.1016/j.ijforecast.2015.12.002

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