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Probabilistic energy forecasting using the nearest neighbors quantile filter and quantile regression [in press]

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

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.



Originalveröffentlichung
DOI: 10.1016/j.ijforecast.2019.06.003
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Jahr 2019
Sprache Englisch
Identifikator ISSN: 0169-2070
KITopen-ID: 1000098733
HGF-Programm 37.98.11 (POF III, LK 01)
Erschienen in International journal of forecasting
Schlagworte Forecasting, Energy, Quantile regression, Nearest neighbors, Data-driven modeling, Energy Lab 2.0, Data mining
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