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The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

Author

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  • Simone Sperati

    (Sustainability & Energy Sources, Ricerca Sistema Energetico (RSE) SpA, Milano 20134, Italy)

  • Stefano Alessandrini

    (Research Applications Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO 80301, USA)

  • Pierre Pinson

    (Electrical Engineering, Technical University of Denmark (DTU), Kgs. Lyngby 2800, Denmark)

  • George Kariniotakis

    (Renewable Energies & SmartGrids, MINES ParisTech, Sophia Antipolis 06904, France)

Abstract

A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (“WIRE”) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

Suggested Citation

  • Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, vol. 8(9), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:9:p:9594-9619:d:55264
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    11. Thorey, J. & Chaussin, C. & Mallet, V., 2018. "Ensemble forecast of photovoltaic power with online CRPS learning," International Journal of Forecasting, Elsevier, vol. 34(4), pages 762-773.
    12. 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.
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    14. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
    15. Roberto Langella & Daniela Proto & Alfredo Testa, 2016. "Solar Radiation Forecasting, Accounting for Daily Variability," Energies, MDPI, vol. 9(3), pages 1-17, March.
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