Probabilistic Forecasting of the Wave Energy Flux

Pierre Pinson, G. Reikard, J.-R. Bidlot

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    Wave energy will certainly have a significant role to play in the deployment of renewable energy generation capacities. As with wind and solar, probabilistic forecasts of wave power over horizons of a few hours to a few days are required for power system operation as well as trading in electricity markets. A methodology for the probabilistic forecasting of the wave energy flux is introduced, based on a log-Normal assumption for the shape of predictive densities. It uses meteorological forecasts (from the European Centre for Medium-range Weather Forecasts – ECMWF) and local wave measurements as input. The parameters of the models involved are adaptively and recursively estimated. The methodology is evaluated for 13 locations around North-America over a period of 15months. The issued probabilistic forecasts substantially outperform the various benchmarks considered, with improvements between 6% and 70% in terms of Continuous Rank Probability Score (CRPS), depending upon the test case and the lead time. It is finally shown that the log-Normal assumption can be seen as acceptable, even though it may be refined in the future.
    Original languageEnglish
    JournalApplied Energy
    Pages (from-to)364-370
    Publication statusPublished - 2012
    EventFifth International Green Energy Conference - University of Waterloo, Waterloo, Ontario, Canada
    Duration: 1 Jun 20103 Jun 2010
    Conference number: 5


    ConferenceFifth International Green Energy Conference
    LocationUniversity of Waterloo
    CityWaterloo, Ontario
    SponsorCanadian Society for Mechanical Engineering
    Internet address

    Bibliographical note

    This article is published in: "Special Issue on Green Energy" in the journal: "Applied Energy".
    It has been presented at two conferences: "Fifth International Green Energy Conference" and "2nd International Energyy 2030 Conference"


    • Wave energy
    • Forecasting
    • Statistical models
    • Adaptive estimation
    • Forecast skill
    • Probabilistic calibration

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