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.
|Publication status||Published - 2012|
|Event||Fifth International Green Energy Conference - University of Waterloo, Waterloo, Ontario, Canada|
Duration: 1 Jun 2010 → 3 Jun 2010
Conference number: 5
|Conference||Fifth International Green Energy Conference|
|Location||University of Waterloo|
|Period||01/06/2010 → 03/06/2010|
|Sponsor||Canadian Society for Mechanical Engineering|
Bibliographical noteThis 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
- Statistical models
- Adaptive estimation
- Forecast skill
- Probabilistic calibration