Machine Learning Based Power Grid Outage Prediction in Response to Extreme Events

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Daniel Felix Ritchie School of Engineering and Computer Science, Electrical and Computer Engineering


Hurricanes, Power grids, Logistics, Predictive models, Neural networks, Regression tree analysis, Wind speed


A machine learning based prediction method is proposed in this paper to determine the potential outage of power grid components in response to an imminent hurricane. The decision boundary, which partitions the components' states into two sets of damaged and operational, is obtained via logistic regression by using a second-order function and proper parameter fitting. Two metrics are examined to validate the performance of the obtained decision boundary in efficiently predicting component outages.

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