1. Dr. Ulrich Focken EWEC 2007 MILAN, 08. May 2007 >>> Optimal Combination of different Wind Power Predictions

2. >>> 2 Overview  Motivation  Weather Models  Classification and Combination  Summary

3. >>> 3 Motivation  Wind power prediction systems commonly use only one single numerical weather prediction model (NWP).  But everyday experience shows: NWP models have strengths and weaknesses in different situations.  Our approach: Optimal combination of weather models adapted to different weather situations.

4. >>> 4 Combining Europe‘s NWP for a better forecast

5. >>> 5 Domains overlap

6. >>> 6 Previento – the physical approach Previento Physical Model: Spatial refinement Thermal stratification Regional upscaling Forecast uncertainty

7. >>> 7 rmse root mean square error (rmse) dayahead- forecast January-October 2005 for single forecasts

8. >>> 8 Rule-based combination is required  “Combine and average: [...] Simple average performs as well as more sophisticated statistical approaches.” Clemen, R.T., Combining forecasts: A review and annotated bibliography, Int. Journal of Forecasting 5 (1989) 559-582.  „Rule-based forecasting: [...] We believe that this procedure will lead to improvements.“ Armstrong,J.S., Combining Forecasts: The End of the Beginning or the Beginning of the End?, Int. Journal of Forecasting 5 (1989) 585-588. use weather information as expert input

9. >>> 9 Combination of wind power forecasts combination tool 1. classification of weather situation 2. optimal combination Model 1 + Previento Model 2 + model X + Previento combined wind power prediction Model n + Previento Model 3 etc.... „CombiTool“

10. >>> 10 How the CombiTool works 1. Classification – Find significant weather situations  Principal Component Analysis Simplifying the dataset of meteorological parameters by reducing multi-dimensional data set to lower dimensions  Clustering Clustering groups similar objects into different subsets (clusters), so that the data in each subset share some common trait. here: similar weather situtation 2. Optimal Combination – Find the best combined forecast Find in each situation (cluster) the optimal weighting factors

11. >>> 11 Results Clustering: Mean of u- und v-component and pressure in clusters low passing North high pressure pmsl [mbar]

12. >>> 12 Weather situation „Cyclone passing – type A“

13. >>> 13 Weather situation „Cyclone passing – type A“

14. >>> 14 Cyclone passing – type A : one model is delayed power [% inst. power] days

15. >>> 15 High pressure Eastern Europe

16. >>> 16 High pressure Eastern Europe: models differ power [% inst. power] days

17. >>> 17 Combination 18. Juli 2005 (+1d,2d,3d)

18. >>> 18 Combination 9. Januar 2005 (+1d,2d,3d)

19. >>> 19 Optimal factors differ from situation to situation normalized average combination factors [%]

20. >>> 20 Accuracy in individual weather situations  using optimal weights for each weather situation leads to considerable improvement best single model rmse [% inst. power] Cyclone passing – type A High pressure Eastern Europe sitation based combination 3.9 % overall rmse 5.0 %

21. >>> 21 Combination in extreme events power [% inst. power] days combination „ combination very benefitial in extreme events

22. >>> 22 Summary  NWP have strengths and weaknesses in different weather situations.  Just putting together forecasts is not sufficient, careful selection needed.  Automatic classification scheme based on methods from synoptic climatology generates useful weather classes.  Optimal combination based on weighting factors for specific weather situations outperforms individual forecasts.  Combination avoids large forecast errors in extreme events

23. >>> 23 Summary  The system will go in operation at in Juli  It will use at least 8 forecasts from 4 different forecast provider as input

24. >>> 24 Thanks for your attention !

25. >>> 25 >>> Contact Dr. Ulrich Focken energy & meteo systems GmbH Marie-Curie Straße 1 26129 Oldenburg ulrich.focken@energymeteo.de www.energymeteo.de