Researchers from John Hopkins and Texas A&M universities say that they have found a way to accurately predict power outages in advance of a hurricane. Computer models developed using data from Hurricane Katrina and four other destructive storms could save utilities substantial amounts of money and help facilitate rapid restoration of power after a storm, they say.

The study, funded by an "anonymous Gulf Coast utility," was recently published in the journal Risk Analysis. "If the power company overestimates, it has spent a lot of unnecessary money," Steven Quiring, a research member and assistant professor of geography at Texas A&M said. "If it underestimates, the time needed to restore power can take several extra days or longer, which is unacceptable to them and the people they serve. So these companies need the best estimates possible, and we think this study can help them make the best possible informed decision."

Damage data from five storms were considered: Dennis (1995), which created about 4,800 outages; Danny (1997), 620 outages; Georges (1998), 1,075 outages; Ivan (2004), 13,500 outages; and Katrina (2005), more than 10,000 outages.

For the worst of these storms, some customers were without power for up to 11 days. The research team collected information about the locations of outages in these previous hurricanes. An outage was defined as permanent loss of power to a set of customers due to activation of a protective device in the power system. Researchers also included information about the power system in each area (including poles and transformers), hurricane wind speeds, wetness of the soil, long-term average precipitation, land use, local topography, and other related factors. This data was then used to train and validate a statistical regression model called a Generalized Additive Model, a particular form of model that can account for nonlinear relationships between the variables.

The study is available at

— Sonal Patel is POWER’s senior writer.