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Comparison of Implicit vs. Explicit Regime Identification in Machine Learning Methods for Solar Irradiance Prediction

Author

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  • Tyler McCandless

    (National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA)

  • Susan Dettling

    (National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA)

  • Sue Ellen Haupt

    (National Center for Atmospheric Research (NCAR), Boulder, CO 80305, USA)

Abstract

This work compares the solar power forecasting performance of tree-based methods that include implicit regime-based models to explicit regime separation methods that utilize both unsupervised and supervised machine learning techniques. Previous studies have shown an improvement utilizing a regime-based machine learning approach in a climate with diverse cloud conditions. This study compares the machine learning approaches for solar power prediction at the Shagaya Renewable Energy Park in Kuwait, which is in an arid desert climate characterized by abundant sunshine. The regime-dependent artificial neural network models undergo a comprehensive parameter and hyperparameter tuning analysis to minimize the prediction errors on a test dataset. The final results that compare the different methods are computed on an independent validation dataset. The results show that the tree-based methods, the regression model tree approach, performs better than the explicit regime-dependent approach. These results appear to be a function of the predominantly sunny conditions that limit the ability of an unsupervised technique to separate regimes for which the relationship between the predictors and the predictand would differ for the supervised learning technique.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:689-:d:316869
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    References listed on IDEAS

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    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. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    3. 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.
    4. McCandless, T.C. & Haupt, S.E. & Young, G.S., 2016. "A regime-dependent artificial neural network technique for short-range solar irradiance forecasting," Renewable Energy, Elsevier, vol. 89(C), pages 351-359.
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    Cited by:

    1. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
    2. Lioua Kolsi & Sameer Al-Dahidi & Souad Kamel & Walid Aich & Sahbi Boubaker & Nidhal Ben Khedher, 2022. "Prediction of Solar Energy Yield Based on Artificial Intelligence Techniques for the Ha’il Region, Saudi Arabia," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    3. Giorgio Guariso & Giuseppe Nunnari & Matteo Sangiorgio, 2020. "Multi-Step Solar Irradiance Forecasting and Domain Adaptation of Deep Neural Networks," Energies, MDPI, vol. 13(15), pages 1-18, August.
    4. Latifa A. Yousef & Hibba Yousef & Lisandra Rocha-Meneses, 2023. "Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions," Energies, MDPI, vol. 16(24), pages 1-27, December.
    5. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
    6. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.

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