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Buildings currently consume about 40% of all the electricity used in the United States, most of them located in urban areas that are growing rapidly. Because electricity generation is the largest source of greenhouse gas emissions in the country, making urban buildings more energy efficient could help mitigate global climate change.
In order to achieve efficient buildings at a city-wide scale, accurate occupancy estimations are crucial. These estimates need to take into account the fact that people move around their cities throughout the day, from home to work, which drives energy consumption for different building types. Now, a model developed by researchers from Berkeley Lab, UC Berkeley, and MIT can do just that. A paper describing the tool, which uses passively collected cellphone data to improve urban scale building occupancy and mobility estimates, was recently published in Nature Communications.
"Understanding building occupancy at an urban-scale allows us to plan better for collective energy use. Like traffic apps that tell you the current state of road congestion, we envision a model that could potentially tell users what the energy demands are in different places and therefore identify bespoke efficiency measures," said Berkeley Lab scientist Marta Gonzalez, who is also a UC Berkeley professor of Civil and Environmental Engineering and co-author of the paper. "The tool could also potentially connect to smart-devices that automatically adjust to the energy demand."
Read the full article from Berkeley Engineering here.
