Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression

Jorge Ángel González Ordiano, Lutz Gröll, Ralf Mikut and Veit Hagenmeyer

International Journal of Forecasting, 2020, vol. 36, issue 2, 310-323

Abstract: Parametric quantile regression is a useful tool for obtaining probabilistic energy forecasts. Nonetheless, traditional quantile regressions may be complicated to obtain using complex data mining techniques (e.g., artificial neural networks), since they are trained using a non-differentiable cost function. This article presents a method that uses a new nearest neighbors quantile filter to obtain quantile regressions independently of the data mining technique utilized and without the non-differentiable cost function. This method is subsequently validated using the dataset from the 2014 Global Energy Forecasting Competition. The results show that the method presented here is able to solve the competition’s task with a similar accuracy to the competition’s winner and in a similar timeframe, but requiring a much less powerful computer. This property may be relevant in an online forecasting service for which the fast computation of probabilistic forecasts using less powerful machines is required.

Keywords: Forecasting; Energy; Quantile regression; Nearest neighbors; Data-driven modeling; Energy Lab 2.0; Data mining (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:36:y:2020:i:2:p:310-323

DOI: 10.1016/j.ijforecast.2019.06.003

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