Articles | Volume 21, issue 2
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-607-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2021
Research article |
| 11 Feb 2021
| 11 Feb 2021
Predicting power outages caused by extratropical storms
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Ilona Láng
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
Alexander Jung
Aalto University, Department of Computer Science, B.O. 11000, 00076 Aalto, Finland
Antti Mäkelä
Finnish Meteorological Institute, B.O. 503, 00101 Helsinki, Finland
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We present a classification method for extratropical cyclones and windstorms and show their impacts on Finland's electricity grid by analysing the 92 most damaging windstorms (2005–2018). The south-west- and north-west-arriving windstorms cause the most damage to the power grid. The most relevant parameters for damage are the wind gust speed and extent of wind gusts. Windstorms are more frequent and damaging in autumn and winter, but weaker wind speeds in summer also cause significant damage.
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Short summary
Predicting the number of power outages caused by extratropical storms is a key challenge for power grid operators. We introduce a novel method to predict the storm severity for the power grid employing ERA5 reanalysis data combined with a forest inventory. The storms are first identified from the data and then classified using several machine-learning methods. While there is plenty of room to improve, the results are already usable, with support vector classifier providing the best performance.
Predicting the number of power outages caused by extratropical storms is a key challenge for...
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