Embedding of time series for the prediction in photovoltaic power plants

The ability to forecast the power produced by renewable energy plants in short and middle terms is a key issue to allow an high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the Trasmission System Operator level, as well as electrical distributors and power system operators level. In this paper we present three techniques based on neural and fuzzy neural networks, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches.

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