11th EMS & 10th ECAM

Berlin, Deutschland

The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and

uncertainty estimationS.Alessandrini, S.Sperati,

G.Decimi,

P.Pinson2, (2DTU, Denmark )

09/2011

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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

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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

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Wind Power forecast system for EPS

NN

EnsemblePower forecast

0-72 hours

0-72 hourswind forecast

(corrected)

ECMWF 51 members (0-72 hours wind forecast)

Power Curve

(theoretical)

Windmeasurements

• 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

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Deterministic Wind Power forecast system used at RSE

Power measurements

Power forecast0-72 hours

ECMWF deterministic forecast model

0-72 hours wind forecast

NN

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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 2008-2011 are supplied by ENEL

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Case Study: wind farm data,meteorological comparison

• Two time periods have been considered• Old EPS and deterministic model version have been tested between 01-2008

and 11-2008 • New EPS and deterministic model version have been tested between 11-2010

and 06-2011

2008 2011

ws (mean) sd2008 6.69 3.112011 7.47 3.55

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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

day1 day2 day3RMSE (normalized) 0.142 0.153 0.162MAE (normalized) 0.10 0.106 0.117Pearson correlation 0.77 0.72 0.67BIAS (normalized) -0.02 -0.02 0.004RMSE (normalized) 0.193 0.194 0.209MAE (normalized) 0.143 0.147 0.155Pearson correlation 0.76 0.76 0.71BIAS (normalized) 0.03 0.03 0.03

2008

2011

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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

day1 day2 day3RMSE (normalized) 0.193 0.194 0.209MAE (normalized) 0.143 0.147 0.155Pearson correlation 0.76 0.76 0.71BIAS (normalized) 0.03 0.03 0.03RMSE (normalized) 0.184 0.192 0.203MAE (normalized) 0.132 0.141 0.151Pearson correlation 0.80 0.77 0.74BIAS (normalized) 0.03 0.04 0.03

2011 - deterministic

2011 - EPS mean

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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 MOS2008 2011 2008 2011

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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

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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>)

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Case study: Recalibrated EPS

2008

2011

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Case study: Ensemble spread (wind)

raw wind data (10m) after MOS

after calibration

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Case study: EPS• The PIT histograms show a more calibrated model (wind)

DAY 1 DAY 2 DAY 3

2008

2011

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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

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Case study: EPS• The PIT histograms show a calibrated model (power)

DAY 1 DAY 2 DAY 3

2008

2011

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Case study: Deterministic errors vs ensemble spread (power)

spread-error correlation

CRPS (kW)

spread-error correlation

CRPS (kW)

spread-error correlation

CRPS (kW)

2008 0.60 75.6 0.56 77.8 0.63 78.82011 0.64 82.5 0.61 84 0.65 92.1

day1 day2 day3

Statistical comparison between average daily spread vs daily RMSE Of deterministic power forecast during the test period

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Case study: Deterministic errors vs ensemble spread (power)

DAY 1 DAY 2 DAY 3

2008

201176%

70%

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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