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![Page 1: ANEMOS Advanced Wind Power Forecasting Operational Challenges and On-line Performance Ignacio Martí 1, Georges Kariniotakis 2, Vincent Genard 2 et al.](https://reader030.fdocuments.us/reader030/viewer/2022032702/56649f495503460f94c6b626/html5/thumbnails/1.jpg)
ANEMOS Advanced Wind Power ForecastingOperational Challenges
and On-line Performance
Ignacio Martí1, Georges Kariniotakis2, Vincent Genard2 et al.1Renewable Energies National Center (CENER), 2 ARMINES
European Wind Energy Conference European Wind Energy Conference Milan, 7 - 10 May 2007
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ANEMOS consortiumANEMOS consortium
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The operational challenge The operational challenge
Steps carried out in the framework of ANEMOS project to develop ANEMOS prediction system: Comparison of existing prediction models. Definition of end users requirements for a
prediction system (TSOs, utilities, promoters, regulatory bodies).
Development of ANEMOS system. Adaptation of existing and new prediction models
for the integration in ANEMOS Go online!.
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Generic configuration of the platform.
Structure of ANEMOS platformStructure of ANEMOS platform
Us
er
Inte
rfac
es
Administration Operators
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CombiCombi
NWPNWP
Methodology for the online Methodology for the online performance analysisperformance analysis
ANEMOS Wind power forecasting modelsANEMOS Wind power forecasting models
ALADINALADIN
Wind farm Wind farm SCADASCADA LocalPredLocalPred PCPCVamemosVamemos NTUANTUA
ANEMOS-AnalysisANEMOS-Analysis
ComparisonComparison
HIRLAMHIRLAMSKIRONSKIRON
PersistencePersistence
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Studied operational casesStudied operational cases
Five cases analysed
for this paper inSpain and France
Denmark
Ireland
Germany
Greece
France
Spain
UKCanada
Commercially
Studied period:July 2006- February
2007
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Selected test casesSelected test cases
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Alaiz highly complex terrainAlaiz highly complex terrainNMAE ALAIZ 20060904 - 20070228
SKIRON
0
5
10
15
20
25
30
35
40
45
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M1NMAE M2NMAE M3
NRMSE ALAIZ 20060904 - 20070228SKIRON
0
10
20
30
40
50
60
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NR
MS
E (
% n
om
ina
l po
we
r)
NRMSE PCNRMSE persistenceNRMSE M1NRMSE M2NRMSE M3
NBIAS ALAIZ 20060904 - 20070228SKIRON
-30
-20
-10
0
10
20
30
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NB
IAS
(%
no
min
al p
ow
er)
NBIAS PCNBIAS persistenceNBIAS M1NBIAS M2NBIAS M3
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NMAE SOTAVENTO 20061105 - 20070221SKIRON
0
5
10
15
20
25
30
35
40
45
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M1NMAE M2NMAE M3
NMAE SOTAVENTO 20060605 - 20070221HIRLAM
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M3
Sotavento medium complex Sotavento medium complex terrainterrain
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NMAE OUPIA 20060705 - 20070228ALADIN
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M3
NMAE OUPIA 20061003-20070228SKIRON
0
5
10
15
20
25
30
35
40
45
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M1NMAE M2NMAE M3
Oupia low complex terrainOupia low complex terrain
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NMAE GUERLEDAN 20060705 - 20070228ALADIN
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M3
NMAE GUERLEDAN 20061003-20070109SKIRON
0
5
10
15
20
25
30
35
40
45
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M1NMAE M2NMAE M3
Guerlédan low complex Guerlédan low complex terrainterrain
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NMAE SAINT SIMON 20061003 - 20070109SKIRON
0
5
10
15
20
25
30
35
40
45
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M1NMAE M2NMAE M3
NMAE SAINT SIMON 20060705 - 20070228ALADIN
0
5
10
15
20
25
30
35
40
45
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
Horizon (h)
NM
AE
(%
no
min
al p
ow
er)
NMAE PCNMAE persistenceNMAE M3NMAE Combi
Saint Simon flat terrainSaint Simon flat terrain
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Which is the best model for Which is the best model for horizons<6h?horizons<6h?
NMAE h<6 PC Persistence Combi M1 M2 M3
ALAIZ 25,25 15,85 14,45 10,16 12,58
SOTAVENTO Hirlam 10,52 8,49 8,81
SOTAVENTO Skiron 16,65 15,11 10,35 8,96 11,66
OUPIA Aladin 14,22 11,87 10,79
OUPIA Skiron 19,06 15,55 13,94 19,23 12,65
SAINT SIMON Aladin 13,40 9,58 8,19 8,82
SAINT SIMON Skiron 26,08 11,09 9,32 11,73 9,12
GUERLEDAN Aladin 17,15 9,88 8,26
GUERLEDAN Skiron 20,48 10,87 11,11 10,10 9,59
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Which is the best model for Which is the best model for 6h<horizon<24h?6h<horizon<24h?
NMAE 6<h<24 PC Persistence Combi M1 M2 M3
ALAIZ Skiron 23,50 29,54 16,47 17,25 20,87
SOTAVENTO Hirlam 12,23 16,54 13,16
SOTAVENTO Skiron 15,19 26,71 13,16 12,46 13,68
OUPIA Aladin 14,67 24,06 15,05
OUPIA Skiron 21,09 27,00 15,79 19,66 19,58
SAINT SIMON Aladin 14,05 17,72 9,66 11,76
SAINT SIMON Skiron 24,60 21,15 10,70 12,85 11,27
GUERLEDAN Aladin 16,87 18,30 10,33
GUERLEDAN Skiron 16,08 20,18 10,37 10,31 10,79
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Which is the best model for Which is the best model for 24h<horizon<48h?24h<horizon<48h?
NMAE 24<h<48 PC Persistence M1 M2 M3
ALAIZ Skiron 28,15 34,76 17,52 15,10 23,08
SOTAVENTO Skiron 18,25 27,87 13,40 14,67 18,22
OUPIA Skiron 21,94 33,72 16,23 19,96 22,46
SAINT SIMON Skiron 27,16 24,04 10,70 12,78 13,08
GUERLEDAN Skiron 19,14 21,70 12,00 11,21 12,75
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NMAE vs Complexity
0
5
10
15
20
25
0 1 1 2 2 3 3 4Terrain complexity
NM
AE
(%
no
min
al p
ow
er)
M1M2M3Lineal (M2)Lineal (M3)Lineal (M1)
Does the terrain complexity Does the terrain complexity affects?affects?
NMAE vs Complexity
0
5
10
15
20
25
0 1 1 2 2 3 3 4Terrain complexity
NM
AE
(%
no
min
al p
ow
er)
M2M3M1Lineal (M1)Lineal (M3)Lineal (M2)
NMAE vs complexity
0
5
10
15
20
25
0 1 2 3 4Degree of complexity
NM
AE
(%
no
min
al p
ow
er)
M1M2M3Lineal (M2)Lineal (M3)Lineal (M1)
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ConclusionsConclusions
More than 6 months of operational experience of ANEMOS prediction system.
5 test wind farms analyzed. Prediction models are complementary,
giving margin for the improvement of the forecast by combination.
Some models are more sensitive than others to the complexity of the terrain (NWP errors).