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Article Dans Une Revue IEEE Transactions on Sustainable Energy Année : 2019

Probabilistic Model for Spatio-Temporal Photovoltaic Power Forecasting

Résumé

Photovoltaic (PV) power generation is characterized by significant variability. Accurate PV forecasts are a prerequisite to securely and economically operating electricity networks, especially in the case of large-scale penetration. In this paper, we propose a probabilistic spatio-temporal model for the PV power production that exploits production information from neighboring plants. The model provides the complete future probability density function of PV production for very short-term horizons (0-6 hours). The method is based on quantile regression and a L1 penalization technique for automatic selection of the input variables. The proposed modeling chain is simple, making the model fast and scalable to direct on-line application. The performance of the proposed approach is evaluated using a real-world test case, with a high number of geographically distributed PV installations and by comparison with state-of-the-art probabilistic methods.
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Dates et versions

hal-01817629 , version 1 (18-06-2018)

Identifiants

Citer

Xwégnon Ghislain Agoua, Robin Girard, Georges Kariniotakis. Probabilistic Model for Spatio-Temporal Photovoltaic Power Forecasting. IEEE Transactions on Sustainable Energy , 2019, 10 (2), pp.780 - 789. ⟨10.1109/TSTE.2018.2847558⟩. ⟨hal-01817629⟩
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