Solar PV Power Forecasting Using Extreme Learning Machine and Information Fusion
Résumé
We provide a learning algorithm combining distributed Extreme Learning Machine and an information fusion rule based on the ag-gregation of experts advice, to build day ahead probabilistic solar PV power production forecasts. These forecasts use, apart from the current day solar PV power production, local meteorological inputs, the most valuable of which is shown to be precipitation. Experiments are then run in one French region, Provence-Alpes-Côte d'Azur, to evaluate the algorithm performance.
Origine : Fichiers produits par l'(les) auteur(s)
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