Smarter Hurricane Prediction to Shorten Power Outages
R. Colin Johnson | Date: 10-21-09 | Comments: 4
- When the next hurricane hits the Gulf states, a new prediction model aims to anticipate the areas that will be hardest hit by power outages, enabling the accurate pre-placement of repair crews.
When the next hurricane hits the Gulf states, a new prediction model
aims to anticipate the areas that will be hardest hit by power outages,
enabling the accurate pre-placement of repair crews.
Gulf-state electric utilities were hit this decade by five devastating
hurricanes, prompting them to search for smarter ways to shorten the
resultant power outages--one of which lasted for 11 days. The best bet
so far is the new outage prediction model, which combines machine
learning with real-time data monitoring, in hopes of reducing the
length of power outages when next a hurricane hits.
"We give forecasts about when and where a hurricane will strike, and
how bad it will be," said engineering professor Seth Guikema at Johns
Hopkins University in Baltimore. "We have a better way of forecasting
how many power outages there will be and where they will be, so that
utility companies can know how many crews to call up and where to
station them."
Crafted in cooperation with Steven Quiring, a professor of geography at
Texas A&M, the new outage prediction model learned from historical
statistics amassed during the power outages caused by past hurricanes
including Dennis (1995), Danny (1997), Georges (1998), Ivan (2004) and
Katrina (2005). The hope is to lessen the length of power outages. For
instance, Ivan created 13,500, Katrina more than 10,000, Dennis 4,800,
Georges 1,075 and Danny 620.
The learning algorithm, called a generalized additive model, learns the
patterns of past outages by dividing the service area into thousands of
grid cells. Using the historical data about outages in those cells, it
creates a predictive statistical model. Then when a new hurricane is
headed that way, the team is able to predict the wind speed in each
grid cells from the past history, allowing utility companies to
pre-position repair teams in what are likely to become the worst hit
areas.
"What is unique about our approach, is it is based on a lot more than
just poles and wind," said Guikema. "For instance, it considers
long-term average precipitation, the current soil moisture, the
topology of the landscape, the amount and kind of vegetation, and other
factors. For example, if you have a really wet summer and then have a
hurricane, you are going to have poles that are easier to blow down and
cause outages."
The generalized additive model learns not only from historical data,
but during each future hurricane will continually refine its accuracy
by comparing its predictions to what actually happened, then updating
the model so that it makes even better predictions next time.
Just recently completed, the researchers are currently waiting until
the next Gulf state hurricane to put the outage prediction model to the
test.
