Use of offsite data to improve short term wind power ramp forecasting

1. Use of offsite data to improve short term wind power ramp forecasting EWEA 2013

2. 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

3. State of the art forecasting methods aim to capture the timing and amplitude of events to a high degree of accuracy. Example Forecast Results Hourly data 24 hours in advance 2

4. GL Garrad Hassan’s Current Forecasting Method NWP Forecast NWP Forecast Historic SCADA Live SCADA Site geography NWP Forecast • 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 Climatology Adaptive statistics Time Series Model adaptation Intelligent Model Combination Wind speed forecast Live SCADA Site geography Power model Model adaptation Power forecast 3

5. 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

6. NWP Data Assimilation (DA) Supplemented with Pattern Recognition: Two Methods Tested • Dynamic Pattern Recognition • Static Pattern Recognition • Trained pattern recognition • Histogram matching • Measurement location • pre-determined • - Untrained pattern • recognition • - Running minimization of • Euclidean distance • - Measurement location • not pre-determined • WRF DA • Pattern • Recognition 5

7. Pattern Recognition – Static (1) ‘Trained’ search space (pre-defined offline): (3) Compare ‘template’ with search space element-wise: Time (0) Time - 1 Time - 2 Closest pattern to observations WS Z1 WS Z2 Pattern 1 SLP WS Z1 (4) Form histogram out of matching matrix, make forecast from mode: WS Z2 Pattern 2 SLP WS Z1 WS Z2 Pattern 3 SLP This pattern’s wind speed= forecast (2) Prepare ‘template’ (real time observations): Time (0) Time - 1 Time - 2 WS Z1 WS Z2 SLP 6

8. Pattern Recognition – Dynamic (1) ‘Untrained’ search space (all previous N observations): (3) Vectorize template, compute/store Euclidean distance between all N obs: T(0) T-1 T-2 WS Z1 D(patternn,template) = WS Z2 Pattern 1 SLP WS Z1 … (4) Make forecast from pattern(s) that minimizes Euclidean distance: WS Z2 SLP D(1) = 15.4 D(2) = 12.3 D(3) = 36.5 D(4) = 0.5 … … D(N) = 12.33 WS Z1 Pattern N WS Z2 SLP (2) Prepare ‘template’ (real time observations): This pattern’s wind speed = forecast T(0) T-1 T-2 WS Z1 WS Z2 SLP 7

9. Range and Diversity of Inputs  More Meaningful Pattern Recognition Sea level pressure Patterns = 1% spread Anomaly range: 0.45 2 Hr MAE: 2.57 m/s Surface Temperature Patterns Anomaly range: 5.48 2 Hr MAE: 1.77 m/s Tall Tower Wind Speed Patterns Anomaly range: 8.91 2 Hr MAE: 1.21 m/s 8

10. Diverse Variables Reduce Error Forecast Error vs. Variable Range 9

11. Diverse Variables Reduce Error Surface Inputs: Smaller range, Higher error Forecast Error vs. Variable Range Vertically-stratified inputs: Diverse range, Lower error 10

12. Best Forecasts • Best forecast requires a blend of both applications Forecast Wind Speed MAE (Mean Absolute Error) NWP/DA 3 2 1 0 50/50 MAE (m/s) PATTREC 0 2 4 6 8 10 12 Horizon (hours) 11

13. 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

14. Conclusions • 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

15. Jeremy ParkesJeremy.Parkes@gl-garradhassan.comPatrick ShawPatrick.Shaw@gl-garradhassan.com Thank You