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A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

Author

Listed:
  • Branko Kosovic

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Sue Ellen Haupt

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Daniel Adriaansen

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Stefano Alessandrini

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Gerry Wiener

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Luca Delle Monache

    (Scripps Institution of Oceanography, University of California at San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA)

  • Yubao Liu

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Seth Linden

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Tara Jensen

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • William Cheng

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Marcia Politovich

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

  • Paul Prestopnik

    (National Center for Atmospheric Research, 1850 Table Mesa Dr, Boulder, CO 80305, USA)

Abstract

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numerical weather prediction and machine-learning methods. While the original system was designed with the primary focus on day-ahead power prediction in support of power trading, the enhanced system provides short-term forecasting for unit commitment and economic dispatch, uncertainty quantification in wind speed prediction with probabilistic forecasting, and prediction of extreme events such as icing. Furthermore, the empirical power conversion machine-learning algorithms now use a quantile approach to data quality control that has improved the accuracy of the methods. Forecast uncertainty is quantified using an analog ensemble approach. Two methods of providing short-range ramp forecasts are blended: the variational doppler radar analysis system and an observation-based expert system. Extreme events, specifically changes in wind power due to high winds and icing, are now forecasted by combining numerical weather prediction and a fuzzy logic artificial intelligence system. These systems and their recent advances are described and assessed.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:6:p:1372-:d:332990
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    References listed on IDEAS

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    1. Cheng, William Y.Y. & Liu, Yubao & Bourgeois, Alfred J. & Wu, Yonghui & Haupt, Sue Ellen, 2017. "Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation," Renewable Energy, Elsevier, vol. 107(C), pages 340-351.
    2. 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.
    3. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
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    2. Yee Van Fan & Zorka Novak Pintarič & Jiří Jaromír Klemeš, 2020. "Emerging Tools for Energy System Design Increasing Economic and Environmental Sustainability," Energies, MDPI, vol. 13(16), pages 1-25, August.
    3. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
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    5. Muhammad Ahsan Zamee & Dongjun Won, 2020. "Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction," Energies, MDPI, vol. 13(23), pages 1-29, December.
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    7. Nathan Oaks Farrar & Mohd Hasan Ali & Dipankar Dasgupta, 2023. "Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review," Energies, MDPI, vol. 16(3), pages 1-25, February.
    8. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    9. Paweł Piotrowski & Inajara Rutyna & Dariusz Baczyński & Marcin Kopyt, 2022. "Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors," Energies, MDPI, vol. 15(24), pages 1-38, December.
    10. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    11. Daniel Akinyele & Abraham Amole & Elijah Olabode & Ayobami Olusesi & Titus Ajewole, 2021. "Simulation and Analysis Approaches to Microgrid Systems Design: Emerging Trends and Sustainability Framework Application," Sustainability, MDPI, vol. 13(20), pages 1-26, October.
    12. Tiago Pinto, 2023. "Artificial Intelligence as a Booster of Future Power Systems," Energies, MDPI, vol. 16(5), pages 1-4, February.
    13. 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.
    14. Kübra Tümay Ateş, 2023. "Estimation of Short-Term Power of Wind Turbines Using Artificial Neural Network (ANN) and Swarm Intelligence," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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