Negative Binomial Regression of Electric Power Outages in Hurricanes
Publication: Journal of Infrastructure Systems
Volume 11, Issue 4
Abstract
Hurricanes can cause extensive power outages, resulting in economic loss, business interruption, and secondary effects to other infrastructure systems. Currently, power companies are unable to accurately predict where outages will occur. Therefore, it is difficult for them to deploy repair personnel and materials, and make other emergency response decisions in advance of an event. This paper describes negative binomial regression models for the number of hurricane-related outages likely to occur in each one square kilometer grid cell and in each zip code in a region due to passage of a hurricane. The models are based on a large Geographic Information System database of outages in North and South Carolina from three hurricanes: Floyd (1999), Bonnie (1998), and Fran (1996). The most useful explanatory variables are the number of transformers in the area, the company affected, maximum gust wind speed, and a hurricane effect. Wind speeds were estimated using a calibrated hurricane wind speed model. Pseudo -squared values and other diagnostic statistics are developed to facilitate model selection with generalized negative binomial models.
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Acknowledgments
The writers would like to thank the National Science Foundation (Grant No. NSFCMS-0196003) for financial support of this research; Zhigang Huang for his help in generating the hurricane wind speed data; and Larry Nunnery (Progress Energy) and Michael Royster (Duke Energy) for providing the outage and inventory data, and consultation on company operations. This support is gratefully acknowledged, but the writers take sole responsibility for the content of the paper.
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 11 • Issue 4 • December 2005
Pages: 258 - 267
Copyright
© 2005 ASCE.
History
Received: Feb 28, 2003
Accepted: Sep 28, 2004
Published online: Dec 1, 2005
Published in print: Dec 2005
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