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Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability

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  • Fouilloy, Alexis
  • Voyant, Cyril
  • Notton, Gilles
  • Motte, Fabrice
  • Paoli, Christophe
  • Nivet, Marie-Laure
  • Guillot, Emmanuel
  • Duchaud, Jean-Laurent

Abstract

Eleven statistical and machine learning tools are analyzed and applied to hourly solar irradiation forecasting for time horizon from 1 to 6 h. A methodology is presented to select the best and most reliable forecasting model according to the meteorological variability of the site. To make the conclusions more universal, solar data collected in three sites with low, medium and high meteorological variabilities are used: Ajaccio, Tilos and Odeillo. The datasets variability is evaluated using the mean absolute log return value. The models were compared in term of normalized root mean square error, mean absolute error and skill score. The most efficient models are selected for each variability and temporal horizon: for the weak variability, auto-regressive moving average and multi-layer perceptron are the most efficient, for a medium variability, auto-regressive moving average and bagged regression tree are the best predictors and for a high one, only more complex methods can be used efficiently, bagged regression tree and the random forest approach.

Suggested Citation

  • Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pa:p:620-629
    DOI: 10.1016/j.energy.2018.09.116
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    20. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
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    22. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    23. Tyler McCandless & Susan Dettling & Sue Ellen Haupt, 2020. "Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction," Energies, MDPI, vol. 13(3), pages 1-14, February.
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