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

Data sets

ERA5 hourly data on pressure levels from 1979 to present H. Hersbach, B. Bell, P. Berrisford, G. Biavati, A. Horányi, J. Muñoz Sabater, J. Nicolas, C. Peubey, R. Radu, I. Rozum, D. Schepers, A. Simmons, C. Soci, D. Dee, and J.-N. Thépaut

The National Forest Inventory (NFI) K. Korhonen, T. Packalen, and A. Kangas

Model code and software

SASSE polygon process R. Tervo

SmartMet Server setup for ERA5 R. Tervo and T. Sirviö

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