Articles | Volume 21, issue 2
Research article
11 Feb 2021
Research article |  | 11 Feb 2021

Predicting power outages caused by extratropical storms

Roope Tervo, Ilona Láng, Alexander Jung, and Antti Mäkelä


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (28 Oct 2020) by Amy Donovan
AR by Roope Tervo on behalf of the Authors (30 Oct 2020)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (18 Nov 2020) by Amy Donovan
RR by Anonymous Referee #1 (02 Dec 2020)
RR by Tim Kruschke (11 Dec 2020)
ED: Publish subject to technical corrections (07 Jan 2021) by Amy Donovan
AR by Roope Tervo on behalf of the Authors (12 Jan 2021)  Author's response   Manuscript 
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.
Final-revised paper