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| Titel |
Improving the calibration of the best member method using quantile regression to forecast extreme temperatures |
| VerfasserIn |
A. Gogonel, J. Collet, A. Bar-Hen |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 5 ; Nr. 13, no. 5 (2013-05-03), S.1161-1168 |
| Datensatznummer |
250018440
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| Publikation (Nr.) |
 copernicus.org/nhess-13-1161-2013.pdf |
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| Zusammenfassung |
| Temperature influences both the demand and supply of electricity and is
therefore a potential cause of blackouts. Like any electricity provider,
Electricité de France (EDF) has strong incentives to model the uncertainty
in future temperatures using ensemble prediction systems (EPSs). However, the
probabilistic representations of the future temperatures provided by EPSs are
not reliable enough for electricity generation management. This lack of
reliability becomes crucial for extreme temperatures, as these extreme
temperatures can result in blackouts. A proven method to solve this problem
is the best member method (BMM). This method improves the representation as a
whole, but there is still room for improvement in the tails of the
distribution. The idea of the BMM is to model the probability distribution of
the difference between the forecast and realization. We improve the error
modeling in BMM using quantile regression, which is more efficient than the
usual two-stage ordinary least squares (OLS) regression. To achieve further
improvement, the probability that a given forecast is the best one can be
modeled using exogenous variables. |
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