Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks

Cyril Voyant (1) , Marc Muselli (1, 2) , Christophe Paoli (1) , Marie Laure Nivet (1) , Philippe Poggi (1) , Pierrick Haurant (1)

1 SPE - Sciences pour l'environnement
2 International Organization for Dew Utilization

The official meteorological network is poor on the island of Corsica: only three sites being about 50 km apart are equipped with pyranometers which enable measurements by hourly and daily step. These sites are Ajaccio (41°55'N and 8°48'E, seaside), Bastia (42°33'N, 9°29'E, seaside) and Corte (42°30'N, 9°15'E average altitude of 486 meters). This lack of weather station makes difficult the predictability of PV power grid performance. This work intends to study a methodology which can predict global solar irradiation using data available from another location for daily and hourly horizon. In order to achieve this prediction, we have used Artificial Neural Network which is a popular artificial intelligence technique in the forecasting domain. A simulator has been obtained using data available for the station of Ajaccio that is the only station for which we have a lot of data: 16 years from 1972 to 1987. Then we have tested the efficiency of this simulator in two places with different geographical features: Corte, a mountainous region and Bastia, a coastal region. On daily horizon, the relocation has implied fewer errors than a “naïve” prediction method based on the persistence (RMSE=1468 Vs 1383Wh/m² to Bastia and 1325 Vs 1213Wh/m² to Corte). On hourly case, the results were still satisfactory, and widely better than persistence (RMSE=138.8 Vs 109.3 Wh/m² to Bastia and 135.1 Vs 114.7 Wh/m² to Corte). The last experiment was to evaluate the accuracy of our simulator on a PV power grid localized at 10 km from the station of Ajaccio. We got errors very suitable (nRMSE=27.9%, RMSE=99.0 W.h) compared to those obtained with the persistence (nRMSE=42.2%, RMSE=149.7 W.h).

Domaines: Informatique; Réseau de neurones [cs.NE]; Energie électrique

Cyril Voyant, Marc Muselli, Christophe Paoli, Marie Laure Nivet, Philippe Poggi, et al.. Predictability of PV power grid performance on insular sites without weather stations: use of artificial neural networks. 24th European Photovoltaic Solar Energy Conference, Sep 2009, Hamburg, Germany. pp.4141 - 4144, ⟨10.4229/24thEUPVSEC2009-5BV.2.35⟩. ⟨hal-00442312⟩