Synergizing Two NWP Models to Improve Hub-Height Wind Speed Forecasts

1. 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 Exhibition
Montreal, Quebec – November 1, 2010

2. Synergizing Two NWP Models to Improve
Hub-Height Wind Speed Forecasts
• Drivers
• Methodology
• Evaluation Criteria
• Data Source
• Results
• Discussions

3. 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

4. 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)

5. Methodology (1)
Continental Scale
NWP
Meso-scale NWP
Wind Forecast
On-line Wind /
Power Data
High Resolution
Geography
Nested Meso-scale NWP
Site Specific
Physical 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.

6. Methodology (2)
GEM
(15-km)
Forecast
Model
Optimal
Combination
Improved
Forecast
NAM
(12-km)
Forecast
Model

7. Methodology (3)
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
ji
m
N
ji
m
TT
jid
jid
HjiU
HYXU
,
,
),(
1
),(
),,(
),,(
2
*)11(*1
NAMGEM
NAMGEM
FF
FWFWIF




8. Methodology (3)
• Relative improvement of combined
forecast (Nielsen et al, 2007):
• Weight on the best of two (Nielsen et al,
2007):
1
12
2
2
1;
1)11(2)11(
1
1

 



 I
IRI
R
IP
1)11(2)11(
)11(1
1 2



IRI
IR
W

9. 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)
Improvement
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
Weight(W1)
I1=0%
I1=5%
I1=10%
I1=15%
W1(I1=0%)
W1(I1=5%)
W1(I1=10%)
W1(I1=15%)

10. Evaluation Criteria
• Root Mean Squared Error (RMSE, Lange,2003)
• Improvement
RMSE
N
e
e e
e x x r x x x x
i
i
N
i
pred meas pred meas pred meas

 
    


1
2 1
2
1
2 2
2 2

   
( )
( ) ( )( ( , )) ( ( ) ( ))
(%)(%)
/
/
NAMGEM
NAMGEMcombined
RMSE
RMSERMSE
IP



11. Data Sources (NWPs)

12. Data Sources (Measurements)
Onshore Met Masts near Great Lakes
– Site1 (80-m)
– Site2 (60-m)
– Site3 (80-m)
– Site4 (60-m)

13. 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)
RMSE(m/s)
GEM
NAM
GEM+NAM

14. 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)
RMSE(m/s)
GEM
NAM
GEM+NAM

15. 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)
RMSE(m/s)
GEM
NAM
GEM+NAM

16. 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)
RMSE(m/s)
GEM
NAM
GEM+NAM

17. 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(%RMSE)
Site1
Site2
site3
Site4

18. 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(%RMSE)
Site1
Site2
site3
Site4

19. 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
WindSpeed(m/s)
Measurement
GEM
NAM
GEM+NAM

20. Discussions
• Importance of forecast aspects
– Trading
– Unit commitment & scheduling
– O&M
• Next step is to see if this approach could improve the
ramp forecasts

21. 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.

22. 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 you
CanWEA 2010, 26th Annual Conference and Exhibition
Montreal, Quebec – November 1, 2010