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

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

International Journal of Forecasting, 2016, vol. 32, issue 3, 1087-1093

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.

Keywords: Probabilistic forecast; Ensemble methods; Renewable energy; Quantile regression; Gradient boosting regression; Quantile regression forest (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (24)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:32:y:2016:i:3:p:1087-1093

DOI: 10.1016/j.ijforecast.2015.11.013

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