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.
Original language | English |
---|---|
Title of host publication | 2018 IEEE Data Science Workshop, DSW 2018 - Proceedings |
Publisher | IEEE |
Pages | 145-149 |
Number of pages | 5 |
ISBN (Print) | 9781538644102 |
DOIs | |
Publication status | Published - 17 Aug 2018 |
MoE publication type | A4 Conference publication |
Event | IEEE Data Science Workshop - Lausanne, Switzerland Duration: 4 Jun 2018 → 6 Jun 2018 |
Workshop
Workshop | IEEE Data Science Workshop |
---|---|
Abbreviated title | DSW |
Country/Territory | Switzerland |
City | Lausanne |
Period | 04/06/2018 → 06/06/2018 |
Keywords
- Multilayer Perceptron
- Neural Network
- Power distribution
- Random Forest Classifier
- Weather Impact