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.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-1665-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2023
Research article |
| 03 May 2023
| 03 May 2023
Probabilistic and machine learning methods for uncertainty quantification in power outage prediction due to extreme events
Prateek Arora
CORRESPONDING AUTHOR
Civil and Urban Engineering Department, New York University, Brooklyn, NY 11201, USA
Luis Ceferino
Civil and Urban Engineering Department, New York University, Brooklyn, NY 11201, USA
Center for Urban Science and Progress, New York University, Brooklyn, NY 11201, USA
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Cited
24 citations as recorded by crossref.
- Artificial Neural Network for Medium Voltage Fault Sensitivity Analysis T. Bragatto et al.
- Fast Probabilistic Seismic Hazard Analysis Through Adaptive Importance Sampling S. Houng & L. Ceferino
- Smart investment framework for energy resilience: A case study of a campus microgrid research facility S. Ullah et al.
- Characterizing the Spatiotemporal Complexity of Power Outages in the U.S. Power Grid: A Reliability Assessment Perspective Q. Yu et al.
- Vulnerability and risk assessment of power distribution networks under typhoon compound hazards: a case study of Fujian Province China H. Chen et al.
- Projected increases in tropical cyclone-induced U.S. electric power outage risk J. Rice et al.
- Prioritizing Urban Areas for the Deployment of Hyperlocal Flood Sensors Using Stakeholder Elicitation and Risk Analysis R. Negri et al.
- Probabilistic Storm and Electric Utility Customer Outage Prediction K. Udeh et al.
- Community power outage prediction modeling for the Eastern United States W. Taylor et al.
- Hurricane Risk of Solar Generation in the United States L. Ceferino & N. Lin
- Uncertainty quantification analysis algorithm for smart grid big data platforms for new energy absorption H. Zhang et al.
- Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model W. Hughes et al.
- Machine learning for power outage prediction during hurricanes: An extensive review K. Fatima et al.
- Two-stage optimal scheduling for flexibility and resilience tradeoff of PV-battery building via smart grid communication X. Liang et al.
- Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities B. Ghasemkhani et al.
- Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management J. Otshwe et al.
- A data-driven approach to predicting power outages during winter storms in the southern U.S. leveraging nonparametric machine learning models J. Lee et al.
- Refined Modeling and Failure Mechanisms of Distribution Pole–Line Systems Considering Nonlinear Wind–Rain Coupling B. Chen et al.
- The impact of weather and environmental factors on power outage vulnerability H. Munabia et al.
- A Quasi-Binomial Regression Model for Hurricane-Induced Power Outages during Early Warning P. Arora & L. Ceferino
- The power reliability event simulator tool (PRESTO): A novel approach to distribution system reliability analysis and applications S. Baik et al.
- Predicting weather-related power outages in large scale distribution grids with deep learning ensembles L. Prieto-Godino et al.
- A Bayesian hierarchical model for power system resilience assessment C. Chaudhary et al.
- An assessment of two wind model uncertainties during storm events affecting Portugal N. Salvação et al.
24 citations as recorded by crossref.
- Artificial Neural Network for Medium Voltage Fault Sensitivity Analysis T. Bragatto et al.
- Fast Probabilistic Seismic Hazard Analysis Through Adaptive Importance Sampling S. Houng & L. Ceferino
- Smart investment framework for energy resilience: A case study of a campus microgrid research facility S. Ullah et al.
- Characterizing the Spatiotemporal Complexity of Power Outages in the U.S. Power Grid: A Reliability Assessment Perspective Q. Yu et al.
- Vulnerability and risk assessment of power distribution networks under typhoon compound hazards: a case study of Fujian Province China H. Chen et al.
- Projected increases in tropical cyclone-induced U.S. electric power outage risk J. Rice et al.
- Prioritizing Urban Areas for the Deployment of Hyperlocal Flood Sensors Using Stakeholder Elicitation and Risk Analysis R. Negri et al.
- Probabilistic Storm and Electric Utility Customer Outage Prediction K. Udeh et al.
- Community power outage prediction modeling for the Eastern United States W. Taylor et al.
- Hurricane Risk of Solar Generation in the United States L. Ceferino & N. Lin
- Uncertainty quantification analysis algorithm for smart grid big data platforms for new energy absorption H. Zhang et al.
- Assessing grid hardening strategies to improve power system performance during storms using a hybrid mechanistic-machine learning outage prediction model W. Hughes et al.
- Machine learning for power outage prediction during hurricanes: An extensive review K. Fatima et al.
- Two-stage optimal scheduling for flexibility and resilience tradeoff of PV-battery building via smart grid communication X. Liang et al.
- Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities B. Ghasemkhani et al.
- Graph Convolutional-Optimization Framework for Carbon-Conscious Grid Management J. Otshwe et al.
- A data-driven approach to predicting power outages during winter storms in the southern U.S. leveraging nonparametric machine learning models J. Lee et al.
- Refined Modeling and Failure Mechanisms of Distribution Pole–Line Systems Considering Nonlinear Wind–Rain Coupling B. Chen et al.
- The impact of weather and environmental factors on power outage vulnerability H. Munabia et al.
- A Quasi-Binomial Regression Model for Hurricane-Induced Power Outages during Early Warning P. Arora & L. Ceferino
- The power reliability event simulator tool (PRESTO): A novel approach to distribution system reliability analysis and applications S. Baik et al.
- Predicting weather-related power outages in large scale distribution grids with deep learning ensembles L. Prieto-Godino et al.
- A Bayesian hierarchical model for power system resilience assessment C. Chaudhary et al.
- An assessment of two wind model uncertainties during storm events affecting Portugal N. Salvação et al.
Saved (final revised paper)
Latest update: 14 May 2026
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.
Power outage models can help utilities manage risks for outages from hurricanes. Our article...
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