Ümit Cali , B.Lange, M.Kurt, C.Möhrlen

1. Development of an Offshore-Specific Wind Power Forecasting Model Based on Ensemble Weather Prediction and Wave Parameters Ümit Cali, B.Lange, M.Kurt, C.Möhrlen

2. Aim & Content • Aim: • The aim of this study is to investigate the role of offshore-specific parameters and ensemble weather prediction system on the accuracy of the wind power forecasting for offshore wind farms. • Content: • Selection of methodology: Artificial Neural Network (ANN) • Site description (Horns Rev Wind Farm) • Evaluation of the new approach for an offshore-specific wind power prediction system • Day Ahead offshore wind power forecasting models • 2 Hours Ahead Short-term offshore wind power forecasting models • Optimization of the models (Combination methods) • Results • Conclusion and Outlook

3. Strategical Activitiy Areas / Fields of the EnBW Renewables GbmH • Hydropower • Biomass • Photovoltaik Baden-Württemberg • Wind Power • Wind Power offshore • Wind Power onshore • Biomass Deutschland • Hydropower • Wind Power • Wind Power Europa

4. General Structure of the Study • Notice: The measured wind power information from Horns Rev was available from February 2005 and July 2006. Hence, the common period for all variables is in this case from February 2005 and July 2006.

5. Site Description of the Horns Rev Offshore Wind Farm

6. Wind Power Forecasting using ANN • Physical Models • Statistical Models • Artificial Intelligence based Models (e.g. ANN)

7. General Structure of the Offshore-Specific Wind Power Forecasting

8. Offshore WPF: Day Ahead Forecasting

9. Offshore WPF: Day Ahead Forecasting • DA_1: Wind Speed and Wind Direction at 10m • DA_9: All Available meteorological parameters from MS EPS • DA_10: Like DA_9, additionally forecasted wave parameters from ECMWF

10. Combination Models: Simple Avg. and 2 ANN for DA_10

11. Overall Results of Day Ahead Experiments

12. General Structure of MS EPS

13. 2 Hours Ahead Offshore Wind Power Forecasting Experiments

14. 2 Hours Ahead Offshore Wind Power Forecasting Experiments

15. Results of 2 Hours Ahead (Short-term) Forecasting

16. Conlusion and Outlook • Conlusion • The new offshore day ahead wind power forecasting using additional oceanographic parameters model brings improvements in the forecast accuracy. (21.3 % of improvement) • Beside integrating the WAM (from ECMWF) input variables, application of the combination model approaches (such as simple averaging and 2 ANN) improves the forecast accuracy up to 26.51 %. • The integration of additional parameters such as wave and wind measurements decreases the forecasting error and increases the improvement of the accuracy up to 27.41 % (for 2 hours ahead models). • Outlook • In future, the influence of the vertical temperature gradient shall be investigated in order to increase the forecast accuracy. • We recommend the regulatory authorities to set some regulations and rules for the actors who are supposed to make such measurements to improve the accuracy of the wind predictions and the reliability of their operations.

17. Thank You ... • Notice: The work was carried out mainly at ISET e.v. with measured data from the offshore wind farm Horns Rev in Denmark in the scope of the project “High Resolution Ensemble for Horns Rev” (HRensembleHR) funded by the Danish PSO Programme 2006-2009.

18. Results of Combination Models: Simple Avg. and 2 ANN