Probabilistic forecasting of wind power at the minute time-scale with Markov-switching autoregressive models

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    Abstract

    Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed.
    Original languageEnglish
    Title of host publicationProceedings of IEEE PMAPS 2008, 'Probabilistic Methods Appllied to power Systems'
    PublisherIEEE
    Publication date2008
    Pages1-8
    ISBN (Print)978-1-9343-2521-6
    Publication statusPublished - 2008
    Event10th International Conference on Probabilistic Methods Applied to Power Systems - Rincón, Puerto Rico
    Duration: 25 May 200829 May 2008
    Conference number: 10
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4912595

    Conference

    Conference10th International Conference on Probabilistic Methods Applied to Power Systems
    Number10
    CountryPuerto Rico
    CityRincón
    Period25/05/200829/05/2008
    Internet address

    Bibliographical note

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