Short-Term Prediction of Electricity Outages Caused by Convective Storms

Roope Tervo*, Joonas Karjalainen, Alexander Jung

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)


Prediction of power outages caused by convective storms, which are highly localized in space and time, is of crucial importance to power grid operators. We propose a new machine learning approach to predict the damage caused by storms. This approach hinges identifying and tracking of storm cells using weather radar images on the application of machine learning techniques. Overall prediction process consists of identifying storm cells from CAPPI weather radar images by contouring them with a solid 35-dBZ threshold, predicting a track of storm cells, and classifying them based on their damage potential to power grid operators. Tracked storm cells are then classified by combining data obtained from weather radar, ground weather observations, and lightning detectors. We compare random forest classifiers and deep neural networks as alternative methods to classify storm cells. The main challenge is that the training data are heavily imbalanced, as extreme weather events are rare.

Original languageEnglish
Article number8751131
Pages (from-to)8618-8626
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number11
Publication statusPublished - 1 Nov 2019
MoE publication typeA1 Journal article-refereed


  • Machine learning
  • multilayer perceptrons (MLPs)
  • power distribution faults
  • radar tracking

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