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Synergizing Two NWP Models to Improve Hub-Height Wind
Speed Forecasts
Hong Liu, Ph.D., ORTECH Power Peter Taylor, Ph.D., Prof., York
University
CanWEA 2010, 26th Annual Conference and ExhibitionMontreal, Quebec – November 1, 2010
Synergizing Two NWP Models to Improve Hub-Height Wind Speed Forecasts
• Drivers• Methodology• Evaluation Criteria• Data Source• Results• Discussions
ORTECH Power
• An engineering/consulting firm that specialized in getting renewable energy projects completed, from project management to permitting to financial analysis onto commissioning.
• ORTECH helps;– investors buy Wind Farms– developers build Wind Farms
Drivers• Two forecast paradigms:
– Statistical– Physical
• Forecast errors dictated by phase error (Lange, 2003; Liu, 2009 )
• Refined NWP modelling limited by data availability (Giebel, 2003, Yu, et al, 2008, Liu, 2009)
• Ensemble forecasts constrained by computational resources (Cutler, et al, 2008, Mohrlen, 2004)
• Synergizing outputs from more than 1 NWP model as an alternative (Marti, 2006, Nielsen et al, 2007)
Methodology (1)Continental Scale
NWP
Meso-scale NWP
Wind Forecast
On-line Wind / Power Data
High Resolution Geography
Nested Meso-scale NWP
Site SpecificPhysical Models
Power Model Wind Farm Specifications
Power Forecast
MOS
MOS
Statistical Models to Replace: Physical Downscaling; Extrapolation of Wind Speed to Hub Height; Conversion of Wind Speed to Power; Spatial Upscaling from a Reference Wind Farm; and MOS.
Methodology (2)
GEM (15-km)
Forecast Model
Optimal Combination
Improved Forecast
NAM(12-km)
Forecast Model
Methodology (3)
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)1,,(),,()1(),,(
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kjiUwkjiUwHjiU
Vertical Level k+1
Vertical Level k
(i,j,k+1)
(i,j,k)
(i+4,j+4,k+1)
(i+4,j+4,k)H
d(i,j)
d(i,j)
Z(i+4,j+4,k+1)
Z(i+4,j+4,k)
(XT,YT)
N
jim
N
jim
TT
jid
jidHjiU
HYXU
,
,
),(1
),(),,(
),,(
2
*)11(*1
NAMGEM
NAMGEM
FFFWFWIF
Methodology (3)• Relative improvement of
combined forecast (Nielsen et al, 2007):
• Weight on the best of two (Nielsen et al, 2007):
1
122
2
1;1)11(2)11(
11
IIRI
RIP
1)11(2)11()11(11 2
IRIIRW
Methodology (4)
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
20%
0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Correlation (R)
Impr
ovem
ent
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
Wei
ght (
W1)
I1=0%
I1=5%
I1=10%
I1=15%
W1(I1=0%)
W1(I1=5%)
W1(I1=10%)
W1(I1=15%)
Evaluation Criteria• Root Mean Squared Error (RMSE, Lange,2003)
• Improvement
RM SEN
e
e e
e x x r x x x x
ii
N
i
p red m eas pred m eas pred m eas
1
2 1
2
1
2 2
2 2
( )
( ) ( )( ( , )) ( ( ) ( ) )
(%)(%)/
/
NAMGEM
NAMGEMcombined
RMSERMSERMSE
IP
Data Sources (NWPs)
Data Sources (Measurements)
Onshore Met Masts near Great Lakes
– Site1 (80-m)– Site2 (60-m)– Site3 (80-m)– Site4 (60-m)
Results (Site1)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
Results (Site2)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
Results (Site3)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
Results (Site4)
1
1.5
2
2.5
3
0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
RM
SE (m
/s)
GEMNAMGEM+NAM
Results (IP - GEM)
-40%
-30%
-20%
-10%
0%
10%
20%
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
IP (%
RM
SE)
Site1Site2site3Site4
Results (IP - NAM)
-40%
-30%
-20%
-10%
0%
10%
20%
3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48
Forecast Horizon (hr)
IP (%
RM
SE)
Site1Site2site3Site4
Which forecast is better?
0
2
4
6
8
10
12
14
13/07/2008 0:00 13/07/2008 12:00 14/07/2008 0:00 14/07/2008 12:00 15/07/2008 0:00
Time
Win
d Sp
eed
(m/s)
MeasurementGEMNAMGEM+NAM
Discussions
• Importance of forecast aspects– Trading– Unit commitment & scheduling– O&M
• Next step is to see if this approach could improve the ramp forecasts
References• Cutler, N., Kepert, J. D., Outhred, H. R. and MacGill, I. F.,
2008, Characterizing Wind Power Forecast Uncertainty with numerical Weather Prediction Spatial Fields, Wind Engineering, 32, 509-524.
• Giebel, G., 2003, The State-of-the-Art in Short-Term Prediction of wind Power - A Literature Overview, Project ANEMOS, Risø National Laboratory.
• Lange, M., 2003, Analysis of the Uncertainty of Wind Power Predictions, PhD Thesis, University Oldenburg, Oldenburg, Germany.
• Liu, H., 2009, Wind Speed Forecasting for Wind Energy Applications, PhD Thesis, York University, Toronto, Ontario, Canada.
• Marti, I., 2006, Evaluation of Advanced Wind Power Forecasting Models – Results of the Anemos Project, European Wind Energy Conference, Athens, Greek.
• Mohrlen, C., 2004, Uncertainty in wind energy forecasting, PhD Thesis, University College Cork, National University of Ireland.
• Nielsen, H. A., Nielsen, T. S. and Madsen H., 2007, Optimal Combination of wind Power Forecasts, Wind Energy, 10: 471-482
• Yu, W, Plante, A., Chardon, L., Benoit, R., Glazer, A., Tran, L. D., Gauthier, F., Petrucci, F., Forcione, A. and Roberge, G., 2008, A Wind Forecasting System for Application in Wind Power Management – Results from One-year Real-Time Tests in Quebec, CanWEA 2008 Annual Conference, Vancouver, Canada.
Synergizing Two NWP Models to Improve Hub-Height Wind Speed
Forecasts
Hong Liu, Ph.D., ORTECH Power Peter Taylor, Ph.D., Prof., York
University
Thank youCanWEA 2010, 26th Annual Conference and
ExhibitionMontreal, Quebec – November 1, 2010
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