Coronavirus Information for the UC San Diego Community

Our leaders are working closely with federal and state officials to ensure your ongoing safety at the university. Stay up to date with the latest developments. Learn more.

A comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction

TitleA comprehensive wind power forecasting system integrating artificial intelligence and numerical weather prediction
Publication TypeJournal Article
Year of Publication2020
AuthorsKosovic B., Haupt S.E, Adriaansen D., Alessandrini S., Wiener G., L. Monache D, Liu Y.B, Linden S., Jensen T., Cheng W., Politovich M., Prestopnik P.
Volume13
Date Published2020/03
Type of ArticleArticle
Accession NumberWOS:000528727500077
KeywordsEnergy & Fuels; grid integration; Machine learning; model; radar; renewable energy; turbine icing; wind energy; wind power forecasting
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.

DOI10.3390/en13061372
Student Publication: 
No
sharknado