Accéder directement au contenu Accéder directement à la navigation

# Comparison of Photovoltaic Production Forecasting Methods

Abstract : A new short-term photovoltaic (PV) power forecasting technique based on a polynomial model is proposed in this paper. This technique has been compared with two forecasting methods. The first method is based on deep learning and uses a recurrent neural network (RNN) to extract features from a two-dimensional matrix of PV generation data. The second method employs the Steadysun solution, which was developed by a French company and gives forecasts for up to 30 minutes. The prediction is based on data from the University of Lille RIZOMM'' plant. The main objective of this study is to show the limits of each method and to validate the proposed technique. To select the best method, three-time levels were considered (10 min, 30 min, and 60 min). The results showed that the RNN has very high accuracy over all horizons, in particular for a 60 minutes time horizon with 6-step ahead where the forecasting accuracy can reach 97 %.
Type de document :
Article dans une revue

https://hal-u-picardie.archives-ouvertes.fr/hal-03740622
Contributeur : Louise DESSAIVRE Connectez-vous pour contacter le contributeur
Soumis le : vendredi 29 juillet 2022 - 16:38:53
Dernière modification le : vendredi 5 août 2022 - 14:54:00

### Identifiants

• HAL Id : hal-03740622, version 1

### Citation

Mohamed Hamza Kermia, Jerome Bosche, Dhaker Abbes. Comparison of Photovoltaic Production Forecasting Methods. International Journal of Renewable Energy Research, IJRER, 2022, 12 (2), pp.1041-1051. ⟨hal-03740622⟩

### Métriques

Consultations de la notice