for more power outages due to increasing extreme weather, according to a 2014 study by Johns Hopkins University. New York City ranked as the most vulnerable major city in the U.S. "By determining where power outages are possible, utility companies and other stakeholders, such as emergency managers, can mobilize resources more efficiently in an effort to reduce restoration times with the goal of improving the long-term resiliency of the state's power distribution system." "While this project is focused on the Con Ed service territory, the tool can be utilized by the state's utilities to forecast wind speeds and wind gusts that could lead to power outages at the county and sub-county level in New York state," according to NYSERDA. The $585,000 project is being supported by a $385,000 grant from the New York State Energy Research and Development Authority, as well as additional funding from Con Ed, which serves about 3.4 million customers in the metropolitan New York City area. The system will aim at predicting sustained winds up to 50 mph, as well weighing other factors including snow and heavy rain. It will use machine learning, a form of artificial intelligence, to analyze real-time weather data from UAlbany's New York State Mesonet, a statewide network of 126 weather stations The system is expected to predict winds up to two days in advance, down to an area of one kilometer (which is about six-tenths of a mile). "That could reduce the length of power outages, as well as reduce the number and expense of 'false alarms' when crews are dispatched and then nothing happens." "We think this could help utilities better stage their repair crews before an event," said Jeff Freedman, a research associate at the university's Atmospheric Sciences Research Center. Set to begin testing this summer, the system will rely on a state network of weather stations and a laser imaging system that could help the state's largest utility, Consolidated Edison, to better predict strong winds that could damage power lines.
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