Journal
NATURAL HAZARDS
Volume 74, Issue 3, Pages 1795-1811Publisher
SPRINGER
DOI: 10.1007/s11069-014-1270-9
Keywords
Data mining; Hurricanes; Power outages; Random forests; Power restoration
Funding
- National Science Foundation [CMMI 0968711, 1149460, SEES 1215872]
- U.S. Department of Energy [BER-FG02-08ER64644]
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [1215872] Funding Source: National Science Foundation
- Div Of Civil, Mechanical, & Manufact Inn
- Directorate For Engineering [1149460] Funding Source: National Science Foundation
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Accurate estimates of the duration of power outages caused by hurricanes prior to landfall are valuable for utility companies and government agencies that wish to plan and optimize their restoration efforts. Accurate pre-storm estimates are also important information for customers and operators of other infrastructures systems, who rely heavily on electricity. Traditionally, utilities make restoration plans based on managerial judgment and experience. However, skillful outage forecast models are conducive to improved decision-making practices by utilities and can greatly enhance storm preparation and restoration management procedures of power companies and emergency managers. This paper presents a novel statistical approach for estimating power outage durations that is 87 % more accurate than existing models in the literature. The power outage duration models are developed and carefully validated for outages caused by Hurricanes Dennis, Katrina, and Ivan in a central Gulf Coast state. This paper identifies the key variables in predicting hurricane-induced outage durations and their degree of influence on predicting outage restoration for the utility company service area used as our case study.
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