1. Forecasting Uncertainty Related to Ramps of Wind Power Production
European Wind Energy Conference, Warsaw, April 2010
Arthur Bossavy, Robin Girard, Georges Kariniotakis
Center for Energy and Processes
MINES-ParisTech/ARMINES

2. Introduction
Need to improve wind power forecasting with focus on extreme situations
Various temporal/space scales
Focus on uncertainty and weather predictability
Distribution tail events
To contribute to
An increased and more secure wind integration to power grid
Lower costs (i.e: reduced imbalances) …

3. Centered prediction intervals of coverage:
Introduction
A problem with usual wind power forecasts
Centered prediction intervals of coverage:
predictions
observations

4. Objectives of the work 1. Improve the reliability
of usual confidence intervals
w.r.t ramp events
2. Forecast confidence intervals to estimate the uncertainty of ramps timing

5. Outline A methodology for ramps detection
A probabilistic model using ramps information
Forecast of ramps timing using ensembles

6. Detection of ramps FILTERING THRESHOLDING Threshold Ramp detected
intensity
timing
Threshold

7. Evolution of the ramp intensity through Denmark
Detection of ramps
Evolution of the ramp intensity through Denmark

8. Outline A methodology for ramps detection
A probabilistic model using ramps information
Forecast of ramps timing using ensembles

9. A probabilistic model using ramps information
Objective:
Produce more reliable probabilistic forecasts by using information on forthcoming ramps

10. 3-stage forecasting process using ramps information
Production of spot forecasts
SCADA
NWP
Spot
forecasting
model
Ramps detection
forecasts
spot
Thresholding
Filtering
Ramps
TIMING
INTENSITY
Probabilistic processing
Probabilistic
forecasting
model
NWP
SCADA
TIMING
INTENSITY
Ramps
Information

11. Case-studies 1 wind farm in Ireland, 1 in Denmark
18 months of data (02/01-08/02 and 01/03-07/04)
Hourly power measures
Hourly wind speed/direction NWP forecasts (10m height).
Probabilistic model based on the Quantile Regression Forests procedure

12. Evaluation measures
Reliability:
Sharpness:

13. Results Wind farm in Denmark Wind farm in Ireland
Forecasts underestimate quantiles
Reliability improved for highest quantile forecasts
Sharpness remains unchanged

14. Results Estimation of the uncertainty may be improved at ramps
Need of more tests: other quantile estimation methods

15. Outline A methodology for ramps detection
A probabilistic model using ramps information
Forecast of ramps timing using ensembles

16. Forecast of ramps timing using ensembles
Objective:
Aggregate ramps information provided by members of a wind power forecasts ensemble

17. Forecast of ramps timing using ensembles
Filtering members of a forecasts ensemble
More than 35
over 51 members predicting this ramp h1 h3 h2

18. Forecast ramps timing using ensembles
Proposal for a probabilistic forecast of ramps timing
Mean value for the ramp timing:
Confidence intervals:

19. Case-studies and evaluation results
Case-studies: 3 wind farms in France
Wind speed forecasts ensemble (51 members from the EPS system of ECMWF)
Random Forest procedure
Evaluation of forecast probabilities:
Brier Score:
Brier Skill Score
w.r.t Climatology:

20. Visualization of confidence intervals
70%
57%
65%
39%
39 members predicting the
increasing ramp
15 members predicting the
decreasing ramp
43%
28%

21. Conclusions
The probabilistic model using ramps information may be valuable when estimating the highest quantiles
The approach based on ensembles provide confidence intervals to forecast ramp occurrence.
Reliability w.r.t Climatology is improved
Need of more experiments

22. Acknowledgments
Project SAFEWIND: « Multi-scale data assimilation, advanced wind modelling and forecasting with emphasis to extreme weather situations for a safe large-scale wind power integration »
Industrial partners of the project for providing data

23. Thank you for your attention!29