Probabilistic Forecasting of the Wave Energy Flux

Publication: Research - peer-reviewJournal article – Annual report year: 2012



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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
StatePublished - 2012
EventFifth International Green Energy Conference - Waterloo, Ontario, Canada


ConferenceFifth International Green Energy Conference
???event.location???University of Waterloo
CityWaterloo, Ontario
SponsorThe Advanced Energy Systems Division of the Canadian Society for Mechanical Engineering (CSME)
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"

CitationsWeb of Science® Times Cited: 23


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