How this Montreal-based AI company is making buildings run better

Source: Matthew Halliday · CBC NEWS · | June 23, 2021

Source: Sam Ramadori, president of BrainBox AI, a Montreal-based company with a mission of optimizing the energy efficiency in older buildings. CHRISTINNE MUSCHI/THE GLOBE AND MAIL

Source: Sam Ramadori, president of BrainBox AI, a Montreal-based company with a mission of optimizing the energy efficiency in older buildings. CHRISTINNE MUSCHI/THE GLOBE AND MAIL

Buildings have a big environmental problem. More than one-third of all greenhouse gas (GHG) emissions come from both the construction of new buildings and the heating and cooling of existing ones, according to the World Building Council. It’s why the United Nations Intergovernmental Panel on Climate Change has identified reducing building emissions as critical to meeting the goals of the 2016 Paris climate agreement.

When it comes to “green” buildings, most people think of the ultramodern, energy-efficient new construction that reaps architectural awards and LEED certifications. However, the real efficiency improvements are found somewhere less glamorous: the aging, workhorse commercial structures.

“Most of our buildings in Canada were built before we even had an energy code,” says Burak Gunay, an assistant professor of building science at Carleton University, whose work focuses on the use of big data and artificial intelligence (AI) in green building design. “There’s no easy way to ignore that if you want to really achieve the GHG targets we want to. New buildings are flashy and exciting, but the big gains will come from optimizing existing buildings.”

Optimizing the energy efficiency in older buildings is the mission of BrainBox AI, a Montreal-based company that has developed technology that connects to a building’s HVAC (heating, ventilation and air conditioning) system. The system uses AI, deep learning and cloud-based computing to pro-actively improve the energy consumption of buildings, dramatically reducing their carbon emissions and saving owners money. The system spends about six to eight weeks “learning” the daily rhythm of the building’s climate and occupancy patterns, based on hundreds of thousands of individual data points, right down to every bathroom fan or open window.

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