1. by quantile regression
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
+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

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) = S b 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 S rt(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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