A Quasi-Binomial Regression Model for Hurricane-Induced Power Outages during Early Warning-Reference-Cited by-同舟云学术

A Quasi-Binomial Regression Model for Hurricane-Induced Power Outages during Early Warning

Published:2024-06 Issue:2 Volume:10 Page:
ISSN:2376-7642
Container-title:ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
language:en
Short-container-title:ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

Author:

Arora Prateek¹^ORCID,Ceferino Luis²

Affiliation:

1. Ph.D. Candidate, Dept. of Civil and Urban Engineering, New York Univ., Brooklyn, NY 11201; Doctoral Track Candidate, Center for Urban Science and Progress, New York Univ., Brooklyn, NY 11201 (corresponding author). ORCID: .

2. Assistant Professor, Dept. of Civil and Urban Engineering, New York Univ., Brooklyn, NY 11201; Assistant Professor, Center for Urban Science and Progress, New York Univ., Brooklyn, NY 11201.

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/AJRUA6.RUENG-1215

Reference86 articles.

1. AJOT (American Journal of Transportation). 2021. “Hurricane Ida caused at least 1.2 million electricity customers to lose power | AJOT.COM.” Accessed September 1 2022. https://ajot.com/news/hurricane-ida-caused-at-least-1.2-million-electricity-customers-to-lose-power>(9).

2. American Community Survey. 2019. “American community survey.” Accessed September 1 2022. https://www.census.gov/programs-surveys/acs.

3. U.S. Resilience to large-scale power outages in 2002–2019

4. Arora P. and L. Ceferino. 2023a. “Could rooftop solar panels and storage have enhanced the electricity resilience during Hurricane Isaias (2020)?” In Proc. ICASP 14. Dublin Ireland: Trinity College Dublin.

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

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