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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 %.
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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


  • HAL Id : hal-03740622, version 1


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⟩



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