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Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control

Author

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  • Jean-Laurent Duchaud

    (Science for the Environment, University of Corsica, UMR CNRS 6134, 20000 Ajaccio, France)

  • Cyril Voyant

    (Science for the Environment, University of Corsica, UMR CNRS 6134, 20000 Ajaccio, France
    Radiotherapy Unit, Castelluccio Hospital, BP 85, 20177 Ajaccio, France)

  • Alexis Fouilloy

    (Science for the Environment, University of Corsica, UMR CNRS 6134, 20000 Ajaccio, France)

  • Gilles Notton

    (Science for the Environment, University of Corsica, UMR CNRS 6134, 20000 Ajaccio, France)

  • Marie-Laure Nivet

    (Science for the Environment, University of Corsica, UMR CNRS 6134, 20000 Ajaccio, France)

Abstract

With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system.

Suggested Citation

  • Jean-Laurent Duchaud & Cyril Voyant & Alexis Fouilloy & Gilles Notton & Marie-Laure Nivet, 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control," Energies, MDPI, vol. 13(14), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:14:p:3565-:d:382948
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    References listed on IDEAS

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