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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Simone Sala & Alfonso Amendola & Sonia Leva & Marco Mussetta & Alessandro Niccolai & Emanuele Ogliari, 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant," Energies, MDPI, vol. 12(23), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:23:p:4520-:d:291657
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    References listed on IDEAS

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    6. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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    Cited by:

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    2. Robert Jane & Gordon Parker & Gail Vaucher & Morris Berman, 2020. "Characterizing Meteorological Forecast Impact on Microgrid Optimization Performance and Design," Energies, MDPI, vol. 13(3), pages 1-23, January.
    3. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    4. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    5. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Florian Ziel, 2020. "Load Nowcasting: Predicting Actuals with Limited Data," Energies, MDPI, vol. 13(6), pages 1-15, March.

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