IEEE Power & Energy Magazine - November/December 2019 - 48

1) Statistical Methods of Probabilistic Forecasts
NWP
Deterministic
Model

Statistical Learning Algorithm
Regression (Quantile, Distributional)
Kernel Density
Analog Ensemble

Marginal/Spatial
Probability
Distributions,
Quantiles, Intervals

2) Statistically Based Scenarios
Statistical Learning
Algorithm
Schaake Shuffle
Copulas

NWP
Deterministic
Model

Statistical
Dependence
Structure

Spatiotemporal
Scenarios,
Quantiles,
Intervals

3) Physically Based Ensemble Forecasts
Multiple NWP Model
Parameterizations
(Multischeme,
Multimodel)

Postprocessing

Power
Conversion
Model

Physical Ensemble
Scenarios, Quantiles,
Intervals, Extremes

4) Perturbation-Based Ensemble Forecasts
NWP
Deterministic
Model

Initial or
Boundary
Condition
Perturbations

Postprocessing
+
Calibration
Kernel Density
Copula

Power
Conversion
Model

Physical Ensemble
Scenarios, Qualtiles,
Intervals, Extremes
for Target Horizon

figure 1. An overview of the four state-of-the-art forecast methodologies to generate probabilistic forecasts.

12
10

Germany
Measurement
Delivery
15% Quantile
85% Quantile
Spread

Spread
of Ensemble

Power (GW)

8
6
4
2

Quantiles With
Fixed Confidence
Level

0
15 Jan. 2016
12:00

16 Jan. 2016 16 Jan. 2016
00:00
12:00
Time (UTC)

17 Jan. 2016
00:00

figure 2. A comparison of uncertainty information between a statistical approach
based on a single NWP (blue dashed lines) and spread (deviation) of multiple NWP
forecasts (orange shading). (Source: Energy & Meteo Systems.)
48

ieee power & energy magazine

the uncertainty of the current scenario. Figure 2 compares the difference in the uncertainty information
as seen in the uncertainty bands
for wind power forecasts for all
German wind plants derived from
a statistical type 1 approach using
a single NWP model and a multimodel (type 3) approach. While the
multimodel spread indicated a high
risk of ramping up around noon,
the single-model quantiles indicated a moderate potential error
because the statistical methods
constructed a constant climatology band around a single forecast.
Extremes and outliers in present
weather situations are not well modeled by these methods.

Evaluating Probabilistic
Forecasts
Evaluating the performance of
probabilistic forecasts is unavoidably more complex than assessing
deterministic forecasts because
november/december 2019



IEEE Power & Energy Magazine - November/December 2019

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - November/December 2019

Contents
IEEE Power & Energy Magazine - November/December 2019 - Cover1
IEEE Power & Energy Magazine - November/December 2019 - Cover2
IEEE Power & Energy Magazine - November/December 2019 - Contents
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IEEE Power & Energy Magazine - November/December 2019 - Cover3
IEEE Power & Energy Magazine - November/December 2019 - Cover4
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