Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors

Jan Dobschinski; Ricardo Bessa; Pengwei Du; Kenneth Geisler; Sue Ellen Haupt; Matthias Lange; Corinna Mohrlen; Dora Nakafuji; Miguel De La Torre Rodriguez

doi:10.1109/MPE.2017.2729100

Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors

Jan Dobschinski
, Ricardo Bessa
, Pengwei Du
, Kenneth Geisler
, Sue Ellen Haupt
, Matthias Lange
, Corinna Mohrlen
, Dora Nakafuji
, Miguel De La Torre Rodriguez

Weather Systems and Assessment Program

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production.

Original language	English
Article number	8070538
Pages (from-to)	40-49
Number of pages	10
Journal	IEEE Power and Energy Magazine
Volume	15
Issue number	6
DOIs	https://doi.org/10.1109/MPE.2017.2729100
State	Published - Nov 1 2017

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10.1109/MPE.2017.2729100

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title = "Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors",

abstract = "It is in the nature of chaotic atmospheric processes that weather forecasts will never be perfectly accurate. This natural fact poses challenges not only for private life, public safety, and traffic but also for electrical power systems with high shares of weather-dependent wind and solar power production.",

author = "Jan Dobschinski and Ricardo Bessa and Pengwei Du and Kenneth Geisler and Haupt, \{Sue Ellen\} and Matthias Lange and Corinna Mohrlen and Dora Nakafuji and \{De La Torre Rodriguez\}, Miguel",

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AU - Haupt, Sue Ellen

AU - Lange, Matthias

AU - Mohrlen, Corinna

AU - Nakafuji, Dora

AU - De La Torre Rodriguez, Miguel

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Uncertainty Forecasting in a Nutshell: Prediction Models Designed to Prevent Significant Errors

Abstract

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