Prediction intervals for global solar irradiation forecasting using regression trees methods
Marie-Laure Nivet and
Renewable Energy, 2018, vol. 126, issue C, 332-340
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)
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:126:y:2018:i:c:p:332-340
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().