Dr. Cathérine Mei β ner a Dr. Arne R. Gravdahl a Dr. Xuan Wu b

Short-term Forecasting using Mesoscale Simulations, Neural Networks and CFD Simulations

EWEA 2012 Annual Event 16-19 April 2012Copenhagen

Dr. Cathérine Meiβnera Dr. Arne R. Gravdahla Dr. Xuan Wub

a WindSim AS, Fjordgaten 15, N-3125 Tønsberg, Norwayb WindSim AMERICAS, 470 Atlantic Avenue, Boston, MA 02210, US

EWEA 2012 Annual Event,Copenhagen

• The forecasting procedure

• Validation on a Chinese wind farm

• Conclusion & Outlook

Content


Understanding how much your wind farm will produce in the next hours

is crucial to make the right decisions, either in the energy market or for

maintenance planning.

WindSim has developed a system coupling • Mesoscale numerical weather forecasts (Mesoscale)• Artificial Neural Networks (ANN) and • Computational Fluid Dynamics (CFD)

This system is under development together with partners from the

energy trading business and will be validated on different sites all over

Europe in the next months.

The Procedure: Coupling Mesoscale, ANN and CFD


Mesoscale hindcasts

Historical wind

measurements

The Procedure: Coupling Mesoscale, ANN and CFD

WindSim Power Production Forecast

GLOBAL FORECAST

MESOSCALE FORECAST

Neural Network correction

WindSim wake model

WindSim CFD Downscaling

Neural Network Training

Set-up period Forecasting mode

CFD look-up tables


The Procedure: Coupling Mesoscale and CFD

Global Models100 - 16 km

e.g. ECMWF, GFS

Regional Models9 - 1 km

e.g. WRF

Micro Model100 - 10 mWindSim

Description of the atmosphericconditions

Accurate descriptionof the local flow fieldand the wake effects


Transfer of mesoscale data into the CFD by using a virtual met mast

solution:

The forecast of one point in the mesoscale model inside the CFD

domain is selected and used to scale the CFD model results at every

turbine position

Advantage: Very fast as CFD look-up tables can be produced in the

set-up phase of the forecasting system and no CFD simulation is

necessary during the actual forecast.

Regional Model Micro ModelWindSim

The Procedure: Coupling Mesoscale and CFD


The Procedure: Why use Artificial Neural Networks?

WRFWRF_ANNMEAS

WRF data has phase and model bias errors in wind speed and direction

Trained networks can be used to correct each forecasted time series from the mesoscale model before it is used in the CFD simulation

January February w

ind

sp

eed

(m

/s)

0

5

10

15

20


The Procedure: The added value of using CFD

500m 20 m

The CFD describes more accurately the local flow field around the turbines

and can therefore downscale the mesoscale model results

The CFD is able to calculate the wake corrected energy production

.


Validation – Chinese wind farm

New legislation in China requires an operational forecasting system

for all wind farms

Validation site in China:

Site with 6 measurement masts and 11 turbines Complexity of the site: steepness up to 45 degrees around the

turbine area and absolute height differences of 1000 m WRF simulations run for 4 months in the winter season on 1 km

resolution WRF results for wind speed and wind direction extracted for every

met mast position in the area



Wind Frequency Rose0°

22.5°

45°

67.5°

90°

112.5°

135°

157.5°

180°

202.5°

225°

247.5°

270°

292.5°

315°

337.5°

0%

10%

20%

30%

40%

WRFMEAS

1. The WRF mesoscale model predicts the monthly mean wind speed

and direction well but the absolute value is too high

WRFMEAS

Mast 2



2. The Neural Networks are able to correct this deviation in wind speed

and wind direction

Forecast

Speed RMSE(m/s)

NN 1 Speed RMSE(m/s)

NN 2 Speed RMSE(m/s)

Forecast Direction

RMSE (deg)

NN 1 Direction

RMSE (deg)

NN 2 Direction

RMSE (deg)

Mast 1 3.7 1.7 1.8 34 15 26

Mast 2 3.7 1.6 2.1 53 16 41

Mast 3 3 1.7 1.3 63 21 43

Mast 4 3.3 2 2.3 61 17 42

Average 3.43 1.75 1.88 52.75 17.25 38



3. The CFD modelling improves the modelling of the wind profile

Mast 3Mast 2



4. The CFD is used to calculate the wind speed at the turbines

Turbine 2



5. The CFD is used to calculate the energy output at the turbines –

including wake effects

Turbine 2


A forecasting system has been set-up coupling Mesoscale, ANN and CFD

The system has been validated on a Chinese wind farm in complex

terrain

Giving an ANN corrected wind speed delivers improved energy

calculation when using CFD compared to using mesoscale data directly

Validation is on going with production data from wind farms in Europe

Searching for partners/users who want to validate the system

Conclusions & Outlook

Dr. Cathérine Mei β ner a Dr. Arne R. Gravdahl a Dr. Xuan Wu b

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