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Prediction intervals for global solar irradiation forecasting using regression trees methods

Cyril Voyant, Fabrice Motte, Gilles Notton, Alexis Fouilloy, Marie-Laure Nivet and Jean-Laurent Duchaud

Renewable Energy, 2018, vol. 126, issue C, 332-340

Abstract: A global horizontal irradiation prediction (from 1 h to 6 h) is performed using 2 persistence models (simple and “smart” ones) and 4 machine learning tools belonging to the regression trees methods family (normal, pruned, boosted and bagged). A prediction band is associated to each forecast using methodologies based on: bootstrap sampling and k-fold approach, mutual information, stationary time series process with clear sky model, quantiles estimation and cumulative distribution function. New reliability indexes (gamma index and gamma test) are built from the mean interval length (MIL) and prediction interval coverage probability (PCIP). With such methods and error metrics, good prediction bands are estimated for Ajaccio (France) with a MIL close to 113 Wh/m2, a PCIP reaching 70% and a gamma index lower than 0.9.

Keywords: Probabilistic forecasts; Bagging; Boosting; Pruning; Mean interval length; Prediction interval coverage probability (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:eee:renene:v:126:y:2018:i:c:p:332-340