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

Arora, Prateek; Ceferino, Luis

doi:https://doi.org/10.5194/nhess-23-1665-2023

Articles | Volume 23, issue 5

https://doi.org/10.5194/nhess-23-1665-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Advances in machine learning for natural hazards risk...

https://doi.org/10.5194/nhess-23-1665-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Supplement

https://doi.org/10.5194/nhess-23-1665-2023-supplement

Data sets

Electric customers without power PowerOutage.us https://poweroutage.us/

All NLCD Land Cover 2019 CONUS Land Cover MRLC https://www.mrlc.gov/viewer/

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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.