1. S.Alessandrini, S.Sperati, G.Decimi,
11th EMS & 10th ECAM
Berlin, Deutschland
The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation
S.Alessandrini, S.Sperati, G.Decimi,
P.Pinson2,
(2DTU, Denmark )
09/2011

2. Outline This work is a prosecution of the presentation of EMS 2010
A comparison of wind power deterministic forecast performances based on old and new version ECMWF GM during two periods (2008, 2011) is shown
The new version of the EPS (ECMWF) is applied to understand if it is possible to increase the performances in the forecast of power prediction accuracy

3. ECMWF: ensemble model
EPS run operationally until day 10 ahead, 51 members (perturbed), ctrl run (not perturbed)
EPS resolutions increased from T399/T255 (60 km) to T639/T319 (32 km) on 26 January 2010
We have studied the different performances, between old and new version, using EPS member spread to predict the deterministic forecast error

4. Wind Power forecast system for EPS
The NN is trained using ensemble mean and measured wind
It is then applied to correct each member
The theoretical power curve is verified against measured wind and power data to be representative of the wind farm
ECMWF
51 members (0-72 hours wind forecast) NN
Wind
measurements
0-72 hours
wind forecast
(corrected)
Power
Curve
(theoretical)
Ensemble
Power forecast
0-72 hours

5. Deterministic Wind Power forecast system used at RSE
ECMWF
deterministic forecast model
0-72 hours wind forecast
Power measurements NN
Power forecast
0-72 hours

6. Case Study: wind farm in Sicily
The wind farm is made of 9 turbines with 850 kW of nominal power (50 m height)
It’s located on a mountain region at around 700 m asl
The wind (50 m agl) and power data of the year are supplied by ENEL

7. Case Study: wind farm data, meteorological comparison
Two time periods have been considered
Old EPS and deterministic model version have been tested between and
New EPS and deterministic model version have been tested between and
2008
2011

8. Case Study: wind farm, deterministic power forecast
Comparison of performances using the two versions of deterministic ECMWF meteorological model (2008->0.25°, 2011->0.15°)
For each model the same forecast scheme is applied
A training period for the NN and a test period to verify performances
CONCLUSION: The increase of resolution doesn’t assure better performances also due to different meteorological conditions of the two periods

9. Case Study: wind farm, deterministic power forecast
Comparison of performances using the new version of deterministic ECMWF meteorological model and EPS mean deterministic forecast
CONCLUSION: the EPS ensemble mean leads to a better performances in power forecast with a gain of quite one day of predictability

10. Case study: EPS power forecast
An horizontal bilinear interpolation is performed using the 4 nearest grid points
A MOS technique (NN) is calibrated on the ensemble mean during the training data set to adjust the 51 members wind speed on the test period.
Before MOS
After MOS
2008
2011
2008
2011

11. Case study: EPS power forecast
The PIT histograms for wind speed on the first 3 days show an overconfident model with both EPS version
The wind speed ensemble spread is to small on the first 3 forecast days
DAY 1
DAY 2
DAY 3
2008
2011

12. Case study: Recalibrated EPS
A logit transform is applied to the members to approach a gaussian distribution
A variance deficit (vd) is adaptively calculated for each forecast interval time (day 1, day 2, day 3)
The average variance of (transformed) ensemble members is compared to the variance of the (transformed) errors over that period.
The variance deficit is then calculated as vd = var(error)/mean(var(ensembles)).
Ec(j) = <E> + vd *(E(j) - <E>)

13. Case study: Recalibrated EPS
2008
2011

14. Case study: Ensemble spread (wind)
raw wind data (10m)
after MOS
after calibration

15. Case study: EPS DAY 1 DAY 2 DAY 3 2008 2011
The PIT histograms show a more calibrated model (wind)
DAY 1
DAY 2
DAY 3
2008
2011

16. Case study: EPS recalibration (power)
The theoretic power curve has been used to compute the ensemble power members because well fit the experimental data
2008
2011

17. Case study: EPS DAY 1 DAY 2 DAY 3 2008 2011
The PIT histograms show a calibrated model (power)
DAY 1
DAY 2
DAY 3
2008
2011

18. Case study: Deterministic errors vs ensemble spread (power)
Statistical comparison between average daily spread vs daily RMSE
Of deterministic power forecast during the test period

19. Case study: Deterministic errors vs ensemble spread (power)
DAY 1
DAY 2
DAY 3
2008
70%
2011
76%

20. Conclusions
The EPS mean can be used to increase deterministic performances (2011 case)
The new ECMWF deterministic model resolution doesn’t necessary guarantees better power forecast
The EPS spread on the first 3 forecast days is too low in order to extract usable information from raw data (even after the MOS) even with the new EPS resolution
After a statistical calibration the ensemble power spread seems to have enough correlation with the deterministic error in order to be used as a predictor of accuracy even after 72 hours
The new EPS version seems to be slightly more accurate for the power error prediction