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GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach

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  • Nagy, Gábor I.
  • Barta, Gergő
  • Kazi, Sándor
  • Borbély, Gyula
  • Simon, Gábor

Abstract

We investigate the probabilistic forecasting of solar and wind power generation in connection with the Global Energy Forecasting Competition 2014. We use a voted ensemble of a quantile regression forest model and a stacked random forest–gradient boosting decision tree model to predict the probability distribution. The raw probabilities thus obtained need to be post-processed using isotonic regression in order to conform to the monotonic-increase attribute of probability distributions. The results show a great performance in terms of the weighted pinball loss, with the model achieving second place on the final competition leaderboard.

Suggested Citation

  • Nagy, Gábor I. & Barta, Gergő & Kazi, Sándor & Borbély, Gyula & Simon, Gábor, 2016. "GEFCom2014: Probabilistic solar and wind power forecasting using a generalized additive tree ensemble approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1087-1093.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:3:p:1087-1093
    DOI: 10.1016/j.ijforecast.2015.11.013
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    Cited by:

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    18. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
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