IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v80y2015icp197-204.html

Short-term predictability of photovoltaic production over Italy

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148115001007
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2015.02.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    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.
    3. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, University Library of Munich, Germany.
    2. Fiordaliso, Antonio, 1998. "A nonlinear forecasts combination method based on Takagi-Sugeno fuzzy systems," International Journal of Forecasting, Elsevier, vol. 14(3), pages 367-379, September.
    3. Lindh, Thomas & Malmberg, Bo, 2007. "Demographically based global income forecasts up to the year 2050," International Journal of Forecasting, Elsevier, vol. 23(4), pages 553-567.
    4. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
    5. Artur Budzyński & Maria Cieśla, 2025. "Enhancing Road Freight Price Forecasting Using Gradient Boosting Ensemble Supervised Machine Learning Algorithm," Mathematics, MDPI, vol. 13(18), pages 1-24, September.
    6. Bloom, David E. & Canning, David & Fink, Gunther & Finlay, Jocelyn E., 2007. "Does age structure forecast economic growth?," International Journal of Forecasting, Elsevier, vol. 23(4), pages 569-585.
    7. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    8. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    9. Kumar, V. & Sunder, Sarang & Sharma, Amalesh, 2015. "Leveraging Distribution to Maximize Firm Performance in Emerging Markets," Journal of Retailing, Elsevier, vol. 91(4), pages 627-643.
    10. Gawusu, Sidique & Zhang, Xiaobing & Yakubu, Sufyan & Debrah, Seth Kofi & Das, Oisik & Bundela, Nishant Singh, 2025. "Optimizing solar photovoltaic system performance: Insights and strategies for enhanced efficiency," Energy, Elsevier, vol. 319(C).
    11. Chellaswamy, C. & Ramesh, R., 2016. "Parameter extraction of solar cell models based on adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 97(C), pages 823-837.
    12. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    13. Robert Fildes & Gary Madden & Joachim Tan, 2007. "Optimal forecasting model selection and data characteristics," Applied Financial Economics, Taylor & Francis Journals, vol. 17(15), pages 1251-1264.
    14. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    15. Ayop, Razman & Tan, Chee Wei, 2017. "A comprehensive review on photovoltaic emulator," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 430-452.
    16. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    17. Ariana Chang & Tian‐Shyug Lee & Hsiu‐Mei Lee, 2024. "Applying sustainable development goals in financial forecasting using machine learning techniques," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(3), pages 2277-2289, May.
    18. Ivo Blohm & Christoph Riedl & Johann Fuller & Orhan Koroglu & Jan Marco Leimeister & Helmut Krcmar, 2012. "The Effects of Prediction Market Design and Price Elasticity on Trading Performance of Users: An Experimental Analysis," Papers 1204.3457, arXiv.org.
    19. Massimo Guidolin & Manuela Pedio, 2019. "Forecasting and Trading Monetary Policy Effects on the Riskless Yield Curve with Regime Switching Nelson†Siegel Models," Working Papers 639, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    20. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:80:y:2015:i:c:p:197-204. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.