Probabilistic Solar Forecasting Using Quantile Regression Models

Abstract : In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h–6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.
Type de document :
Article dans une revue
Energies, MDPI, 2017, 10 (10), 〈10.3390/en10101591〉
Liste complète des métadonnées

http://hal.univ-reunion.fr/hal-01617867
Contributeur : Alain Bastide <>
Soumis le : mardi 3 juillet 2018 - 13:10:36
Dernière modification le : jeudi 12 juillet 2018 - 16:32:06

Fichier

Probabilistic_solar_forecastin...
Fichiers éditeurs autorisés sur une archive ouverte

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Collections

Citation

Philippe Lauret, Mathieu David, Hugo Pedro. Probabilistic Solar Forecasting Using Quantile Regression Models. Energies, MDPI, 2017, 10 (10), 〈10.3390/en10101591〉. 〈hal-01617867〉

Partager

Métriques

Consultations de la notice

75

Téléchargements de fichiers

5