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

Branko Kosovic, Sue Ellen Haupt, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, Seth Linden, Tara Jensen, William Cheng, Marcia Politovich and Paul Prestopnik
Additional contact information
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

Energies, 2020, vol. 13, issue 6, 1-16

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.

Keywords: grid integration; machine learning; renewable energy; turbine icing; wind power forecasting; wind energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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