Articles | Volume 21, issue 2
Nat. Hazards Earth Syst. Sci., 21, 607–627, 2021
https://doi.org/10.5194/nhess-21-607-2021
Nat. Hazards Earth Syst. Sci., 21, 607–627, 2021
https://doi.org/10.5194/nhess-21-607-2021

Research article 11 Feb 2021

Research article | 11 Feb 2021

Predicting power outages caused by extratropical storms

Roope Tervo et al.

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Cited articles

Allen, M., Fernandez, S., Omitaomu, O., and Walker, K.: Application of hybrid geo-spatially granular fragility curves to improve power outage predictions, J. Geogr. Nat. Disast., 4, 1–6, https://doi.org/10.4172/2167-0587.1000127, 2014. a
Autonomio: Talos (software), available at: http://github.com/autonomio/talos., last access: 22 June 2020. a
Barredo, J. I.: Normalised flood losses in Europe: 1970–2006, Nat. Hazards Earth Syst. Sci., 9, 97–104, https://doi.org/10.5194/nhess-9-97-2009, 2009. a
Bergstra, J. and Bengio, Y.: Random search for hyper-parameter optimization, J. Mach. Learn. Res., 13, 281–305, 2012. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a, b
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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.
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