meteodyn cfd micro scale modeling statistical learning neural network wind power forecasting

7/30/2019 Meteodyn CFD Micro Scale Modeling Statistical Learning Neural Network Wind Power Forecasting

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Optimal combination of CFD modeling and statistical

learning for shortterm wind power forecasting

Stphane SANQUER & Jrmie JUBAN-meteodyn


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Wind power depends on the volability of the wind

Two times scale are relevant : one for the wind turbine controle (up

to few sec.), one for the integration of power in the grid (minutes to

weeks)

Why a forecast ?

-Optimise the planning of conventional power plants (3-10h)

-Optimise the value of produced electricity in the market (0-48h)

-Schedule the maintenance of the farm and the transmission lines

(day to week)


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The model can be physical, statistical or twice

Very short term statistical approaches use scada as input (lookahead time 6h always includes a Numerical

Weather Prediction system (NWP) and sometimes a Model

Output Statistic system to optimize the forecast (MOS)

Source : Anemos Project The State-Of-The-Art in

Short-Term Prediction of Wind Power


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Average NMAE for 12 hours forecast horizon vs RIXSource: Best Practice in Short-Term Forecasting. A Users Guide

Gregor Giebel(Ris National Laboratory, DTU), George Kariniotakis(Ecole des Mines de Paris)

12 models tested on

various terrains to

consider the local effects

Errors increase with the terrain complexity.

Terrain modeling can be introduced to improve the

performance of the forecast system.


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To define an optimal combination of both physical and statistical

modeling in order to reach the highest forecast performance

To use a learning model ( black box ) based on a data set of

couples measurement/prediction. Here we use a Artificial Neural

Network (ANN)

To minimize the prediction error by introducing automatic

error corrections while keeping the advantage

of the full physical modeling


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Global model0.125 or 0.5 degrees

Resolution

GFS, ECM WF

Mesoscale model .1 km to 15 km

resolution

WRF.

Microscale

model

25 m resolution

Meteodyn WT

Statistical

modelling

DATA


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Mesoscale models compute the wind above the ground with a

resolution from 1 km to 5 km.

Mesoscale models consider the thermal effects on the boundary

layer behaviors. The NWP data defines the stability class at eachtime step.

Mesoscale models can not compute well enough the effects of

complex terrains and should be mixed with microscale models.

Microscale computations are carried out for various stability classes


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The mesoscale points are transfered to each wind turbine thanks to

the speed coefficients obtained by the CFD model

Local effects taken into account : Orography, Land-use

The windspeed coefficients allow the statistical correction of NWP

data and power curves correction, by using met mast measurements.

Calibration takes into account seasonal variations (snow, foliage

density, )


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Resolution

GFS, ECM WF


resolution

WRF.

Microscale

model

25 m resolution

Meteodyn WT

Statistical

modelling

DATA


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Resolution

GFS, ECM WF


resolution

WRF.

Microscale

model

25 m resolution

Meteodyn WT

Statistical

modelling

DATA


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How to define Neural Network Architecture?

(number of layers, number of neurons)

Increasing complexity

Map several inputs to an output

Input: Forecast power, NWP variables andproduction data

Output: wind power or wind speed

The supervised mapping function is learnt from data


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Define three setsA Testing set choose architecture (testing error)

A Training set training the network (training error)

Finally, a validation set computes true error.

Training Error

Testing error

Expected

minimum error


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Wind Farm in China with a complex terrain and weather

regimesLearning period : 06/2010 to 02/2012

Testing period : 03/2012 to 11/2012

Forecast horizons : +6h to 46h

Forecast steps : 15 min. Runs :4/day

Input variables

NWP : V, Dir, S, T, r,Patm

Park production


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Mesoscale modeling is used to compute the wind

above the site

Model GFS/WRF

Resolution 5 km


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Wind speed and production are computed

by considering all the relevant parameters Orography and roughness of terrains

Density of air

Power curves

Wake effects

CFD


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After learning of the ANN model, the production is forecast

and compared to the real production Production is globally well forecasted

Some time lags are observed


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ANN model reduce forecasting errors of pure physical approach

Improvements on MAE and RMSE are respectively 5% and 16%

RMSE reduced to16% bound

MAE reduced to

10.5% bound


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RMSE is roughly constant and depends slightly on the look ahead time

ANN model benefits are the same in the ranges 6h-30h and 22h-46h

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1516

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6h-30h 22h-46h

RMSE

WT

WT+ANN


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An optimal combination of statistical and physical modeling

is central to high performance forecasting

Even for complex terrains, as soon as micro-CFD modeling

is performed, Even with weather regime, by coupling NWP

with Statistical learning for short term wind power forecasting,

RMSE about 15% can be achieved for horizons in the range

6h-48h. MAE reach 10% bound as for flat terrains.

Introducing advanced statistical learning leads to significant

improvement over a pure (even advanced) physical approach


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meteodyn cfd micro scale modeling statistical learning neural network wind power forecasting

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