Predicting Electricity Outages Caused by Convective Storms

Roope Tervo, Joonas Karjalainen, Alexander Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

4 Citations (Scopus)
153 Downloads (Pure)


We consider the problem of predicting power outages in an electrical power grid due to hazards produced by convective storms. These storms produce extreme weather phenomena such as intense wind, tornadoes and lightning over a small area. In this paper, we discuss the application of state-of-the-art machine learning techniques, such as random forest classifiers and deep neural networks, to predict the amount of damage caused by storms. We cast this application as a classification problem where the goal is to classify storm cells into a finite number of classes, each corresponding to a certain amount of expected damage. The classification method use as input features estimates for storm cell location and movement which has to be extracted from the raw data. A main challenge of this application is that the training data is heavily imbalanced as the occurrence of extreme weather events is rare. In order to address this issue, we applied SMOTE technique.

Original languageEnglish
Title of host publication2018 IEEE Data Science Workshop, DSW 2018 - Proceedings
Number of pages5
ISBN (Print)9781538644102
Publication statusPublished - 17 Aug 2018
MoE publication typeA4 Article in a conference publication
EventIEEE Data Science Workshop - Lausanne, Switzerland
Duration: 4 Jun 20186 Jun 2018


WorkshopIEEE Data Science Workshop
Abbreviated titleDSW


  • Multilayer Perceptron
  • Neural Network
  • Power distribution
  • Random Forest Classifier
  • Weather Impact


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