Articles | Volume 23, issue 5
Research article
03 May 2023
Research article |  | 03 May 2023

Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events

Prateek Arora and Luis Ceferino

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Cited articles

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AJOT: Hurricane Ida caused at least 1.2 million electricity customers to lose power | AJOT.COM, (last access: 21 September, 2022), 2021. a
Arab, A., Khodaei, A., Khator, S. K., and Han, Z.: Electric Power Grid Restoration Considering Disaster Economics, IEEE Access, 4, 639–649,, 2016. a
Bjarnadottir, S., Li, Y., and Stewart, M. G.: Hurricane Risk Assessment of Power Distribution Poles Considering Impacts of a Changing Climate, J. Infrastruct. Sys., 19, 12–24,, 2013. a, b, c
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a, b, c
Short summary
Power outage models can help utilities manage risks for outages from hurricanes. Our article reviews the existing outage models during hurricanes and highlights their strengths and limitations. Existing models can give erroneous estimates with outage predictions larger than the number of customers, can struggle with predictions for catastrophic hurricanes, and do not adequately represent infrastructure failure's uncertainties. We suggest models for the future that can overcome these challenges.
Final-revised paper