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| Titel |
Probabilistic regional wind power forecasts based on calibrated Numerical Weather Forecast ensembles |
| VerfasserIn |
Stephan Späth, Lueder von Bremen, Constantin Junk, Detlev Heinemann |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250088873
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| Publikation (Nr.) |
 EGU/EGU2014-3050.pdf |
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| Zusammenfassung |
| With increasing shares of installed wind power in Germany, accurate forecasts of wind speed
and power get increasingly important for the grid integration of Renewable Energies.
Applications like grid management and trading also benefit from uncertainty information.
This uncertainty information can be provided by ensemble forecasts. These forecasts often
exhibit systematic errors such as biases and spread deficiencies. The errors can be reduced by
statistical post-processing.
We use forecast data from the regional Numerical Weather Prediction model COSMO-DE
EPS as input to regional wind power forecasts. In order to enhance the power forecast, we
first calibrate the wind speed forecasts against the model analysis, so some of the model’s
systematic errors can be removed. Wind measurements at every grid point are usually not
available and as we want to conduct grid zone forecasts, the model analysis is the best target
for calibration.
We use forecasts from the COSMO-DE EPS, a high-resolution ensemble prediction system
with 20 forecast members. The model covers the region of Germany and surroundings with a
vertical resolution of 50 model levels and a horizontal resolution of 0.025 degrees
(approximately 2.8 km). The forecast range is 21 hours with model output available
on an hourly basis. Thus, we use it for shortest-term wind power forecasts. The
COSMO-DE EPS was originally designed with a focus on forecasts of convective
precipitation.
The COSMO-DE EPS wind speed forecasts at hub height were post-processed by
nonhomogenous Gaussian regression (NGR; Thorarinsdottir and Gneiting, 2010), a
calibration method that fits a truncated normal distribution to the ensemble wind speed
forecasts. As calibration target, the model analysis was used.
The calibration is able to remove some deficits of the COSMO-DE EPS. In contrast to the
raw ensemble members, the calibrated ensemble members do not show anymore
the strong correlations with each other and the spread-skill relationship improves
significantly.
The deficits of the raw ensemble weather prediction such as a bad spread-skill relationship
and high correlation of members propagate to the power forecast, even when the simulated
wind power is aggregated over the whole grid zone.
By using the calibrated ensemble for production of the power forecasts, we are able to
increase the probabilistic reliability of the resulting probabilistic wind power forecasts. The
ensemble members are better distinguishable than before.
Our results show the possibility of using an Ensemble Prediction System, that was designed
with a focus on probabilistic precipitation forecasts, for wind power forecasts. In order to
gain reliable probabilistic wind power forecasts, it was first necessary to conduct a statistical
post-processing of the ensemble forecasts.
References:
Thorarinsdottir, T., and T. Gneiting (2010), Probabilistic forecasts of wind speed:
ensemble model output statistics by using heteroscedastic censored regression,
Journal of the Royal Statistical Society: Series A(Statistics in Society), 173(2),
371–388. |
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