Multi-Horizon Data-Driven Wind Power Forecast: From Nowcast to 2 Days-Ahead

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    Abstract

    Generation uncertainty is an obvious challenge posed by renewable energy sources such as wind power with effects spawning from stability threats, to economic losses. Datadriven forecasting methods draw increasing attention due to the amount of data available, flexibility and cost-effectiveness among other factors. However, there are concerns regarding effective feature selection and tuning of these models since common naive approaches focus on Pearson or Shapley. This papers uses the development of an active power forecaster for a wind turbine to conduct a thorough sensitivity analysis addressing how different sampling rates, machine learning (ML) methods, features and hyperparameters influence accuracy. Which is computed with the Root-Mean Squared Error and compared against Persistence. The selected ML-methods are Random Forest and Long-Short Term Memory Artificial Neural Networks. The forecasters are multi-horizon & multi-output model targeting 1 minute, 1 hour, 5 hours and 2 days ahead by using sampling rates of 1 second, 1 minute, 5 minutes and 1 hour respectively. The results show which method is more suitable for which horizon and provides insight into which features reduce RMSE of the best performers, whose average is 10, 13, 17 and 25 % for each horizon respectively. The conclusions of the sensitivity analysis can be applied for regions with highly volatile weather, such as coastal areas.
    Original languageEnglish
    Title of host publicationProceedings of 2021 International Conference on Smart Energy Systems and Technologies
    Number of pages6
    PublisherIEEE
    Publication date2021
    ISBN (Print)978-1-7281-7660-4
    DOIs
    Publication statusPublished - 2021
    Event4th International Conference on Smart Energy Systems and Technologies - University of Vaasa, Vaasa, Finland
    Duration: 6 Sept 20218 Sept 2021
    Conference number: 4
    https://sites.uwasa.fi/sest2021/
    http://sites.univaasa.fi/sest2021/

    Conference

    Conference4th International Conference on Smart Energy Systems and Technologies
    Number4
    LocationUniversity of Vaasa
    Country/TerritoryFinland
    CityVaasa
    Period06/09/202108/09/2021
    Internet address

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