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Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting

Author

Listed:
  • Sue Ellen Haupt

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Tyler C. McCandless

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Susan Dettling

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Stefano Alessandrini

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Jared A. Lee

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Seth Linden

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • William Petzke

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Thomas Brummet

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Nhi Nguyen

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Branko Kosović

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Gerry Wiener

    (Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA)

  • Tahani Hussain

    (Energy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, Kuwait)

  • Majed Al-Rasheedi

    (Energy and Building Research Center, Kuwait Institute for Scientific Research, Kuwait City 13109, Kuwait)

Abstract

A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component adds value to the system. KREPS is an example of a fully integrated state-of-the-science forecasting system for renewable energy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:1979-:d:346592
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    References listed on IDEAS

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    1. Branko Kosovic & Sue Ellen Haupt & Daniel Adriaansen & Stefano Alessandrini & Gerry Wiener & Luca Delle Monache & Yubao Liu & Seth Linden & Tara Jensen & William Cheng & Marcia Politovich & Paul Prest, 2020. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction," Energies, MDPI, vol. 13(6), pages 1-16, March.
    2. Mari R. Tye & Sue Ellen Haupt & Eric Gilleland & Christina Kalb & Tara Jensen, 2019. "Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait," Energies, MDPI, vol. 12(23), pages 1-17, November.
    3. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    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.
    5. 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.
    6. AL-Rasheedi, Majed & Gueymard, Christian A. & Al-Khayat, Mohammad & Ismail, Alaa & Lee, Jared A. & Al-Duaj, Hamad, 2020. "Performance evaluation of a utility-scale dual-technology photovoltaic power plant at the Shagaya Renewable Energy Park in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    7. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    8. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    9. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
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    Cited by:

    1. Lorenzo Donadio & Jiannong Fang & Fernando Porté-Agel, 2021. "Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast," Energies, MDPI, vol. 14(2), pages 1-17, January.
    2. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    3. Gwanggil Jeon, 2022. "Artificial Intelligence Approaches for Energies," Energies, MDPI, vol. 15(18), pages 1-3, September.
    4. Isaac Gallardo & Daniel Amor & Álvaro Gutiérrez, 2023. "Recent Trends in Real-Time Photovoltaic Prediction Systems," Energies, MDPI, vol. 16(15), pages 1-17, July.

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