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

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

MetService ePD - the kitchen-sink approach

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

72149 observations (~4 years)

72149 observations (~4 years)

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

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

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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