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Dr. Ulrich Focken EWEC 2007 MILAN, 08. May 2007

>>>. Optimal Combination of different Wind Power Predictions. Dr. Ulrich Focken EWEC 2007 MILAN, 08. May 2007. Overview. Motivation Weather Models Classification and Combination Summary. Motivation.

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Dr. Ulrich Focken EWEC 2007 MILAN, 08. May 2007

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

  2. Overview • Motivation • Weather Models • Classification and Combination • Summary

  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. Combining Europe‘s NWP for a better forecast

  5. Domains overlap

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

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

  8. use weather information as expert input 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.

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

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

  12. Weather situation „Cyclone passing – type A“

  13. Weather situation „Cyclone passing – type A“

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

  15. High pressure Eastern Europe

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

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

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

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

  20. Combination in extreme events combination power [% inst. power] days • combination very benefitial in extreme events

  21. 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

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

  23. Thanks for your attention !

  24. >>> ContactDr.Ulrich Fockenenergy & meteo systems GmbHMarie-Curie Straße 126129 Oldenburgulrich.focken@energymeteo.dewww.energymeteo.de

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