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Use of offsite data to improve short term wind power ramp forecasting. EWEA 2013. Contents. Background and general forecasting method How can offsite measurements be used? Pattern recognition methods Optimal selection of variables for pattern recognition Impact to ramp forecasts - PowerPoint PPT Presentation
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Use of offsite data to improve short term wind power ramp forecastingEWEA 2013
Contents
• Background and general forecasting method• How can offsite measurements be used?• Pattern recognition methods• Optimal selection of variables for pattern recognition• Impact to ramp forecasts• Conclusions
1
Example Forecast ResultsState of the art forecasting methods aim to capture the timing and amplitude of events to a high degree of accuracy.
Hourly data 24 hours in advance
2
NWPForecast
GL Garrad Hassan’s Current Forecasting Method
• Optimised combination of NWP suppliers• Incorporation of mesoscale models• Regular live feedback from the wind farm• “Learning” Algorithms for:• Meteorology• Power models
Suite of Models
Powermodel
Powerforecast
Modeladaptation
Modeladaptation
Wind speedforecast
HistoricSCADA
LiveSCADA
NWPForecastNWP
Forecast
Adaptive statistics ClimatologyTime Series
Intelligent Model Combination
LiveSCADA
Sitegeography
Sitegeography
3
How can offsite measurements be used?Traditional NWP Data Assimilation
• High resolution mesoscale model (WRF)• GL GH system to process real-time observations (last 6 hours) and perform
objective analysis (OA) on the boundary conditions• Forecast accuracy improvement limited by the relative density of measurement
points compared to the model domain
4
– Trained pattern recognition
– Histogram matching
– Measurement location pre-determined
- Untrained pattern recognition
- Running minimization of Euclidean distance
- Measurement location not pre-determined
Static Pattern Recognition
Dynamic Pattern Recognition
NWP Data Assimilation (DA) Supplemented with Pattern Recognition: Two Methods Tested
WRF DA
Pattern Recognition
5
Pattern Recognition – Static
4.1 m/s 3.1 m/s 2.2 m/s
3.8 m/s 5.2 m/s 6.5 m/s
1003 mb 1013 mb 1018 mb
Time (0) Time - 1 Time - 2
SLP
WS Z2
WS Z1
(2) Prepare ‘template’ (real time observations):
(1) ‘Trained’ search space (pre-defined offline):
4.2 m/s 3.5 m/s 2.1 m/s
8.8 m/s 7.2 m/s 6.5 m/s
1015 mb 1017 mb 1018 mb
Time (0) Time - 1 Time - 2
5.2 m/s 4.5 m/s 3.1 m/s
7.8 m/s 8.2 m/s 1.5 m/s
1016 mb 1013 mb 1011 mb
1.2 m/s 2.5 m/s 3.1 m/s
2.8 m/s 5.2 m/s 7.5 m/s
1005 mb 1007 mb 1008 mbP
att
ern
1P
att
ern
3P
att
ern
2
1 1 1
3 3 1
3 2 1
(3) Compare ‘template’ with search space element-wise:
Closest pattern to observations
(4) Form histogram out of matching matrix, make forecast from mode:
This pattern’s wind speed= forecast
6
WS Z1
WS Z2
SLP
WS Z1
WS Z2
SLP
WS Z1
WS Z2
SLP
Pattern Recognition – Dynamic
D(1) = 15.4D(2) = 12.3D(3) = 36.5D(4) = 0.5……D(N) = 12.33
4.1 m/s 3.1 m/s 2.2 m/s
3.8 m/s 5.2 m/s 6.5 m/s
1003 mb 1013 mb 1018 mb
T(0) T-1 T-2
(2) Prepare ‘template’ (real time observations):
(1) ‘Untrained’ search space (all previous N observations):
4.2 m/s 3.5 m/s 2.1 m/s
8.8 m/s 7.2 m/s 6.5 m/s
1015 mb 1017 mb 1018 mb
T(0) T-1 T-2
5.2 m/s 4.5 m/s 3.1 m/s
7.8 m/s 8.2 m/s 1.5 m/s
1016 mb 1013 mb 1011 mb
1.2 m/s 2.5 m/s 3.1 m/s
2.8 m/s 5.2 m/s 7.5 m/s
1005 mb 1007 mb 1008 mbP
att
ern
1P
att
ern
N…
(3) Vectorize template, compute/store Euclidean distance between all N obs:
D(patternn,template) = 2
1
)( i
n
ii pattobs
(4) Make forecast from pattern(s) that minimizes Euclidean distance:
This pattern’s wind speed = forecast
7
WS Z1
WS Z2
SLP
WS Z1
WS Z2
SLP
WS Z1
WS Z2
SLP
WS Z1
WS Z2
SLP
Range and Diversity of Inputs More Meaningful Pattern Recognition
Anomaly range: 0.45
2 Hr MAE: 2.57 m/s
Anomaly range: 8.91
2 Hr MAE: 1.21 m/s
Anomaly range: 5.48
2 Hr MAE: 1.77 m/s
Tall Tower Wind Speed Patterns
Surface Temperature Patterns
= 1% spreadSea level pressure Patterns
8
Diverse Variables Reduce Error
9
Forecast Error vs. Variable Range
Diverse Variables Reduce Error
Surface Inputs:Smaller range,Higher error
Vertically-stratified inputs:Diverse range,Lower error
10
Forecast Error vs. Variable Range
11
0 2 4 6 8 10 12
MA
E (
m/s
)
3
2
1
0
Forecast Wind Speed MAE (Mean Absolute Error)NWP/DA
50/50
PATTREC
Horizon (hours)
Best Forecasts• Best forecast requires a blend of both applications
Impact to Ramp Forecasts
Time history of forecasts and actual production, showing our original forecasts, and the new best blend of existing forecasting techniques with pattern matching.
The optimum blend showed improvement in ramp forecasting of:• 15% improvement in ramp capture scores• 10% reduction in false alarms• 2% reduction in MAE (Mean Absolute Error) as % of capacity
• Use of high fidelity regional offsite data, especially vertically distributed wind speeds, can add value in short term wind and ramp predictions
• NWP data assimilation can be rapidly augmented with simple pattern recognition searching for ramp triggers
• A priori template pattern matching performs well, but dynamic (rolling) pattern matching is less restrictive and casts a wider net
• Anomaly pattern matching works best with variables of higher dynamic range• Surface measurements deviate less per day, towers and profilers offer
additional value in this design
12
Conclusions
