Drexel Researchers Present a Machine Learning Approach for Predicting Philadelphia’s Future Energy Use.
Drexel Researchers Present a Machine Learning Approach for Predicting Philadelphia’s Future Energy Use.
As Philadelphia strives to meet greenhouse gas emissions goals established in its 2050 Plan, a better understanding of how zoning can play a role in managing building energy use could set the city up for success. Researchers in Drexel University’s College of Engineering are hoping a machine learning model they’ve developed can support these efforts by helping to predict how energy consumption will change as neighborhoods evolve.
In 2017, the city set a goal of becoming carbon neutral by 2050, led in large part by a reduction in greenhouse gas emissions from building energy use – which accounted for nearly three-quarters of Philadelphia’s carbon footprint at the time. But the key to meeting this mark lies not just in establishing sustainable energy use practices for current buildings, but also incorporating energy use projections into zoning decisions that will direct future development.
And the challenge for Philadelphia, one of the oldest cities in the country, is that building types vary widely — as does their energy use. So planning for more efficient energy use at the City level is not a problem with a one-size-fits-all solution.
Read more at Drexel University
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