Hybrid methodology for hourly global radiation forecasting in Mediterranean area - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Renewable Energy Année : 2013

Hybrid methodology for hourly global radiation forecasting in Mediterranean area

Cyril Voyant
Marc Muselli
  • Fonction : Auteur
  • PersonId : 841625
Christophe Paoli
Marie Laure Nivet
  • Fonction : Auteur
  • PersonId : 865253

Résumé

The renewable energies prediction and particularly global radiation forecasting is a challenge studied by a growing number of research teams. This paper proposes an original technique to model the insolation time series based on combining Artificial Neural Network (ANN) and Auto-Regressive and Moving Average (ARMA) model. While ANN by its non-linear nature is effective to predict cloudy days, ARMA techniques are more dedicated to sunny days without cloud occurrences. Thus, three hybrids models are suggested: the first proposes simply to use ARMA for 6 months in spring and summer and to use an optimized ANN for the other part of the year; the second model is equivalent to the first but with a seasonal learning; the last model depends on the error occurred the previous hour. These models were used to forecast the hourly global radiation for five places in Mediterranean area. The forecasting performance was compared among several models: the 3 above mentioned models, the best ANN and ARMA for each location. In the best configuration, the coupling of ANN and ARMA allows an improvement of more than 1%, with a maximum in autumn (3.4%) and a minimum in winter (0.9%) where ANN alone is the best.
Fichier principal
Vignette du fichier
manuscript_new.pdf (1.14 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00750486 , version 1 (10-11-2012)

Identifiants

Citer

Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet. Hybrid methodology for hourly global radiation forecasting in Mediterranean area. Renewable Energy, 2013, pp.1. ⟨10.1016/j.renene.2012.10.049⟩. ⟨hal-00750486⟩
239 Consultations
222 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More