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
Publication date2012
Volume93
Pages364-370
ISSN0306-2619
DOIs
StatePublished

Conference

ConferenceFifth International Green Energy Conference
Number5
CountryCanada
CityWaterloo, Ontario
Period01/06/1003/06/10
SponsorThe Advanced Energy Systems Division of the Canadian Society for Mechanical Engineering (CSME)
Internet addresshttp://www.igec.uwaterloo.ca/

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: 7

Keywords

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