Predicting Electricity Outages Caused by Convective Storms

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A4 Artikkeli konferenssijulkaisussa
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Date

2018-08-17

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Department of Computer Science

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Language

en

Pages

5
145-149

Series

2018 IEEE Data Science Workshop, DSW 2018 - Proceedings

Abstract

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.

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Keywords

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

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DOI

10.1109/DSW.2018.8439906

Citation

Tervo, R, Karjalainen, J & Jung, A 2018, Predicting Electricity Outages Caused by Convective Storms . in 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings ., 8439906, IEEE, pp. 145-149, IEEE Data Science Workshop, Lausanne, Switzerland, 04/06/2018 . https://doi.org/10.1109/DSW.2018.8439906

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https://urn.fi/URN:NBN:fi:aalto-201812106157

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[article-cris] Perustieteiden korkeakoulu / SCI