Adaptive post-processing of short-term wind forecasts for energy applications
Article first published online: 2 AUG 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Volume 14, Issue 3, pages 317–325, April 2011
How to Cite
Sweeney, C. and Lynch, P. (2011), Adaptive post-processing of short-term wind forecasts for energy applications. Wind Energ., 14: 317–325. doi: 10.1002/we.420
- Issue published online: 19 APR 2011
- Article first published online: 2 AUG 2010
- Manuscript Accepted: 21 JUN 2010
- Manuscript Revised: 4 JUN 2010
- Manuscript Received: 18 DEC 2009
- wind forecast;
- wind energy;
- adaptive filtering;
- statistical post-processing
We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the forecast skill. The method applied corrective weights to a set of forecasts generated using several post-processing methods. The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared with observed data, and skill scores were calculated to allow comparison between different post-processing methods. The total root mean square error performance of the composite forecast is superior to that of any of the individual methods. Copyright © 2010 John Wiley & Sons, Ltd.