Wind forecasting by quantile regression

1. Wind forecasting by quantile regression Dr. Geoffrey Pritchard University of Auckland

3. Short term (within 2 hours) • The persistence forecast (“no change”) is hard to beat by much. • Important to indicate situation-dependent uncertainty • awareness of risks • probabilistic forecast: scenarios, full pdf

4. MetService ePD - the kitchen-sink approach

5. Persistence forecast: 30min x 5 -2hr 0 +30min TP TP-5 TP-4 TP-3 TP-2 TP-1 • Half-hourly data • Wind forecast is actual output in TP-5 • Actual wind observed

6. 72149 observations (~4 years)

7. 72149 observations (~4 years)

10. Quantile regression Model for the t-quantile (0 < t < 1) of the conditional distribution of a response variable: Q(t) = Sb i (t) xi coefficients explanatory variables Our xi will be (nonlinear) spline basis functions of current power.

12. Quantile regression fitting • To fit observations yi : minimize Srt(yi - b i (t) xi ) where rt is the function t-1 t • Reduces to linear programming.

15. Additional predictor variables • Improve (?) quantile models by adding terms with • wind direction • barometric pressure • time of day • recent power variability • etc. • In single-scenario forecasting, get little improvement on persistence. • In scenario generation, extra variables may help identify low- and high-uncertainty situations.

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