Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

Simone Sala, Alfonso Amendola, Sonia Leva, Marco Mussetta, Alessandro Niccolai and Emanuele Ogliari
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Simone Sala: Eni S.p.A., via Felice Maritano 26, San Donato Milanese, 20097 Milano, Italy
Alfonso Amendola: Eni S.p.A., via Felice Maritano 26, San Donato Milanese, 20097 Milano, Italy
Sonia Leva: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
Marco Mussetta: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
Alessandro Niccolai: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
Emanuele Ogliari: Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy

Energies, 2019, vol. 12, issue 23, 1-19

Abstract: The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

Keywords: photovoltaic power; forecasting; PV; machine learning; deep learning; nowcasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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