Articles | Volume 23, issue 5
https://doi.org/10.5194/nhess-23-1665-2023
https://doi.org/10.5194/nhess-23-1665-2023
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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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-975', Anonymous Referee #1, 11 Nov 2022
    • AC1: 'Reply on RC1', Prateek Arora, 19 Jan 2023
  • RC2: 'Comment on egusphere-2022-975', Anonymous Referee #2, 15 Dec 2022
    • AC2: 'Reply on RC2', Prateek Arora, 19 Jan 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (20 Jan 2023) by Vitor Silva
AR by Prateek Arora on behalf of the Authors (17 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (22 Feb 2023) by Vitor Silva
RR by Anonymous Referee #1 (27 Feb 2023)
RR by Anonymous Referee #2 (27 Mar 2023)
ED: Publish subject to minor revisions (review by editor) (27 Mar 2023) by Vitor Silva
AR by Prateek Arora on behalf of the Authors (27 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (28 Mar 2023) by Vitor Silva
ED: Publish as is (31 Mar 2023) by Philip Ward (Executive editor)
AR by Prateek Arora on behalf of the Authors (31 Mar 2023)  Manuscript 
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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.
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