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Open AccessArticle

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

1
Eni S.p.A., via Felice Maritano 26, San Donato Milanese, 20097 Milano, Italy
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Dipartimento di Energia, Politecnico di Milano, 20156 Milano, Italy
*
Author to whom correspondence should be addressed.
Energies 2019, 12(23), 4520; https://doi.org/10.3390/en12234520
Received: 29 October 2019 / Revised: 22 November 2019 / Accepted: 26 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
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. View Full-Text
Keywords: photovoltaic power; forecasting; PV; machine learning; deep learning; nowcasting photovoltaic power; forecasting; PV; machine learning; deep learning; nowcasting
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Sala, S.; Amendola, A.; Leva, S.; Mussetta, M.; Niccolai, A.; Ogliari, E. Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant. Energies 2019, 12, 4520.

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