A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

Tasnuba Binte Jamal; Samiul Hasan

doi:10.1007/s13753-023-00529-3

Volume 14 Issue 6

Dec. 2023

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Tasnuba Binte Jamal, Samiul Hasan. A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages[J]. International Journal of Disaster Risk Science, 2023, 14(6): 995-1010. doi: 10.1007/s13753-023-00529-3

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Tasnuba Binte Jamal, Samiul Hasan. A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages[J]. International Journal of Disaster Risk Science, 2023, 14(6): 995-1010. doi: 10.1007/s13753-023-00529-3

Citation:

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A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages

doi: 10.1007/s13753-023-00529-3

Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, 32816, USA

Funds:

The authors are grateful to the U.S. National Science Foundation for the Grant CMMI-1832578 to support the research presented in this article. However, the authors are solely responsible for the findings presented here.

Accepted Date: 2023-12-16
Publish Date: 2024-01-08

Abstract

Abstract

Major disasters such as wildfire, tornado, hurricane, tropical storm, and flooding cause disruptions in infrastructure systems such as power and water supply, wastewater management, telecommunication, and transportation facilities. Disruptions in electricity infrastructure have negative impacts on sectors throughout a region, including education, medical services, financial services, and recreation. In this study, we introduced a novel approach to investigate the factors that can be associated with longer restoration time of power service after a hurricane. Considering restoration time as the dependent variable and using a comprehensive set of county-level data, we estimated a generalized accelerated failure time (GAFT) model that accounts for spatial dependence among observations for time to event data. The model fit improved by 12% after considering the effects of spatial correlation in time to event data. Using the GAFT model and Hurricane Irma’s impact on Florida as a case study, we examined: (1) differences in electric power outages and restoration rates among different types of power companies—investor-owned power companies, rural and municipal cooperatives; (2) the relationship between the duration of power outage and power system variables; and (3) the relationship between the duration of power outage and socioeconomic attributes. The findings of this study indicate that counties with a higher percentage of customers served by investor-owned electric companies and lower median household income faced power outage for a longer time. This study identified the key factors to predict restoration time of hurricane-induced power outages, allowing disaster management agencies to adopt strategies required for restoration process.
- Generalized accelerated failure time model,
- Hurricanes,
- Investor-owned power companies,
- Median income,
- Power outage,
- Restoration time

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References(62)

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