Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production

Abstract : In recent years, the penetration of photovoltaic (PV) generation in the energy mix of several countries has significantly increased thanks to policies favoring development of renewables and also to the significant cost reduction of this specific technology. The PV power production process is characterized by significant variability, as it depends on meteorological conditions, which brings new challenges to power system operators. To address these challenges it is important to be able to observe and anticipate production levels. Accurate forecasting of the power output of PV plants is recognized today as a prerequisite for large-scale PV penetration on the grid. In this paper, we propose a statistical method to address the problem of stationarity of PV production data, and develop a model to forecast PV plant power output in the very short term (0-6 hours). The proposed model uses distributed power plants as sensors and exploits their spatio-temporal dependencies to improve forecasts. The computational requirements of the method are low, making it appropriate for large-scale application and easy to use when on-line updating of the production data is possible. The improvement of the normalized root mean square error (nRMSE) can reach 20% or more in comparison with state-of-the-art forecasting techniques
Type de document :
Article dans une revue
IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. 〈10.1109/TSTE.2017.2747765〉
Liste complète des métadonnées

Littérature citée [41 références]  Voir  Masquer  Télécharger

https://hal-mines-paristech.archives-ouvertes.fr/hal-01581946
Contributeur : Brigitte Hanot <>
Soumis le : mardi 5 septembre 2017 - 12:55:00
Dernière modification le : mercredi 18 juillet 2018 - 16:47:41

Fichier

paper_agk.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Xwégnon Ghislain Agoua, Robin Girard, Georges Kariniotakis. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy , IEEE, 2018, 9 (2), pp. 538 - 546. 〈10.1109/TSTE.2017.2747765〉. 〈hal-01581946〉

Partager

Métriques

Consultations de la notice

345

Téléchargements de fichiers

282