Increasing Certainty - Combination methods for reliable wind production forecasts

1. Increasing Certainty -Combination methods for reliable wind production forecasts Jeremy Parkes jeremy.parkes@gl-garradhassan.comEWEA 2011, Tuesday 15 March 2011

2. Contents • Background and general forecasting method • Why combine distributions to calculate forecast uncertainty? • Producing forecast distributions • Optimal combination of forecast distributions • Producing forecast power probability levels from combined distributions • Results • Conclusions

3. GH Forecaster Current Forecasting Method NWP Forecast NWP Forecast Historic SCADA Live SCADA Site geography NWP Forecast • Optimised combination of NWP suppliers • Incorporation of mesoscale models • Regular live feedback from the wind farm • “Learning” Algorithms for: • Meteorology • Power models Suite of Models Climatology Adaptive statistics Time Series Model adaptation Intelligent Model Combination Wind speed forecast Live SCADA Site geography Power model Model adaptation Power forecast

4. Current Probabilistic Forecast • Existing methods do not account for correlation of weather models • Hourly data 24 hours in advance

5. Why combine distributions? • Accuracy of component forecasts for different meteorological conditions • Correlation of weather models

6. Calculating Forecast Distributions from Deterministic Wind Speed Forecasts • Wind speed distributions assumed normal • Calculated from real wind speed data

7. Calculating Forecast Distributions from Ensemble Wind Speed Forecasts • Ensemble member spread correlated to actual spread, but post-processing required

8. Optimal Combination of Forecast Distributions • Distribution combination • Forecast distribution correlation matrix (Pearson coefficient) • Optimal weightings via Normal Model[1] • Covariance of errors of forecast distributions • 1. Clemen RT, Winkler RL. Combining probability distributions from experts in risk. Risk Analysis 1999; 19:187-203.

9. Forecast Power Probability Levels from Distributions • Model inputs: • Forecast wind speed distribution • Power model for central estimate • Required probability level • Transform wind speed distribution via power model • Power distribution

10. Results - Example Probabilistic Forecast

11. Results - Probability Level Accuracy

12. Conclusions • Knowledge of forecast uncertainty is important for decision makers (e.g. for energy traders, grid operators) • Ensemble post-processing is necessary to give accurate distributions • Multi-model ensembles provide the best probabilistic power forecasts • Distribution combination methods reflect correlation of multiple weather models, and are sensitive to different weather conditions • Over short periods of time, combination of distributions gives more reliable probabilistic wind production forecasts than previous methods

13. Any questions?jeremy.parkes@gl-garradhassan.comSee us at stand 7521/7529 Hall 7 Authors:Beatrice Greaves, Jonathan Collins, Jeremy Parkes, Lars Landberg