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Short-term predictability of photovoltaic production over Italy

Author

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  • De Felice, Matteo
  • Petitta, Marcello
  • Ruti, Paolo M.

Abstract

Photovoltaic (PV) power production increased drastically in Europe throughout the last years. Since about the 6% of electricity in Italy comes from PV, an accurate and reliable forecasting of production would be needed for an efficient management of the power grid. We investigate the possibility to forecast daily PV electricity production up to ten days without using on-site measurements of meteorological variables. Our study uses a PV production dataset of 65 Italian sites and it is divided in two parts: first, an assessment of the predictability of meteorological variables using weather forecasts; second, an analysis of predicting solar power production through data-driven modelling. We calibrate Support Vector Machine (SVM) models using available observations and then we apply the same models on the weather forecasts variables to predict daily PV power production. As expected, cloud cover variability strongly affects solar power production, we observe that while during summer the forecast error is under the 10% (slightly lower in south Italy), during winter it is abundantly above the 20%.

Suggested Citation

  • De Felice, Matteo & Petitta, Marcello & Ruti, Paolo M., 2015. "Short-term predictability of photovoltaic production over Italy," Renewable Energy, Elsevier, vol. 80(C), pages 197-204.
    • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:197-204
    DOI: 10.1016/j.renene.2015.02.010
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    References listed on IDEAS

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    1. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    2. Sandrolini, L. & Artioli, M. & Reggiani, U., 2010. "Numerical method for the extraction of photovoltaic module double-diode model parameters through cluster analysis," Applied Energy, Elsevier, vol. 87(2), pages 442-451, February.
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    Citations

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    1. Bett, Philip E & Thornton, Hazel E. & Troccoli, Alberto & De Felice, Matteo & Suckling, Emma & Dubus, Laurent & Saint-Drenan, Yves-Marie & Brayshaw, David J., 2019. "A simplified seasonal forecasting strategy, applied to wind and solar power in Europe," Earth Arxiv kzwqx, Center for Open Science.
    2. De Felice, Matteo & Soares, Marta Bruno & Alessandri, Andrea & Troccoli, Alberto, 2019. "Scoping the potential usefulness of seasonal climate forecasts for solar power management," Renewable Energy, Elsevier, vol. 142(C), pages 215-223.
    3. Xiaomei Wu & Chun Sing Lai & Chenchen Bai & Loi Lei Lai & Qi Zhang & Bo Liu, 2020. "Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction," Energies, MDPI, vol. 13(14), pages 1-21, July.
    4. Gulin, Marko & Pavlović, Tomislav & Vašak, Mario, 2016. "Photovoltaic panel and array static models for power production prediction: Integration of manufacturers’ and on-line data," Renewable Energy, Elsevier, vol. 97(C), pages 399-413.
    5. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    6. Bracco, Stefano & Delfino, Federico & Ferro, Giulio & Pagnini, Luisa & Robba, Michela & Rossi, Mansueto, 2018. "Energy planning of sustainable districts: Towards the exploitation of small size intermittent renewables in urban areas," Applied Energy, Elsevier, vol. 228(C), pages 2288-2297.
    7. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    8. Marco Pierro & Fabio Romano Liolli & Damiano Gentili & Marcello Petitta & Richard Perez & David Moser & Cristina Cornaro, 2022. "Impact of PV/Wind Forecast Accuracy and National Transmission Grid Reinforcement on the Italian Electric System," Energies, MDPI, vol. 15(23), pages 1-28, November.

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