Emerging developments in artificial intelligence (AI) have prompted a technology company to launch a subsidiary focused on optimizing energy operations for commercial and residential buildings to become more sustainable.
Trane Technologies launched Montreal-based BrainBox AI Lab to find solutions to reduce energy consumption by analyzing data from heating, ventilation and air conditioning systems to understand and then predict how a building will operate throughout the day.
“It’s a bit like the movie Back to the Future‚” Jean-Simon Venne, BrainBox AI’s president and founder, told BNN Bloomberg in a Wednesday interview. “We have the capability to go in the future and see what’s not working, and then we’re coming back in the present, and we’re changing the present to build a better future. So that’s how we use AI.”
The AI predicts building temperatures and energy usage, enabling real-time optimization to reduce energy consumption. The AI system acts as a virtual engineer, enhancing productivity by predicting and solving equipment issues before they arise.
“What we’re doing is we’re taking that data and we’re using the capability of AI to give us the prediction of what will be happening in your building over the next few hours,” said Venne. “Having this prediction we then know exactly (when) it’s going to be a bit too hot, a bit too cold, and how much energy you’re going to be spending to maintain the desired temperature in your building. That prediction is then used to optimize the building in real time. We could move from reactive control to a preemptive control and shave up to 25 per cent of energy saving and generate a lot of emission reduction.”
A multidisciplinary team of technical experts, including software engineers, data scientists, AI researchers, machine learning developers and AI engineers will continue to advance autonomous control systems, predictive models, and algorithms aimed at reducing emissions through smarter energy use.
A room or studio for example can heat up from either the heat from sun rays beaming through windows or equipment, like cameras or laundry machines left on for an extended period during peak times. The AI will be able to gather data from routine scenarios for when a room was too hot before to find efficiencies to keep the room at a cool temperature in the future by lowering energy consumption. That can help reduce greenhouse gas emissions (GHG) produced from buildings.
“When you’re trying to save emissions, you want to basically, of course, save that kilowatt or the quantity of cubic or metre of gas that you’re consuming in any given time,” said Venne. “You also want to know how is the kilowatt manufactured? Is the kilowatt that you’re consuming right now in your building, coming from windmill or a coal power generation plant? That information is computed in the AI, and we basically know when we should save that kilowatt.”
“Of course, the AI is making sure that we’re saving it at a time where the electron and the kilowatt are not so green. To say like dirty instead of being green. We optimize the money that that kilowatt is costing you by shaving some and we’re also making sure to do it at the time of day where the kilowatt is dirty instead of green. So, we’re having that double effect. So we’re winning on both fronts.”
Canada has over 15 million residential buildings and over 480,000 commercial and institutional buildings, including offices, retail and warehouses, according to a report from Environment and Natural Resources Canada. Canada’s homes and buildings account for 13 per cent of GHG emissions, due to the combustion of fossil fuels for space and water heating.
Electricity use for cooling, lighting and appliances brings the total to 18 per cent. The buildings sector includes varied businesses, many of which are small and medium sized enterprises including home and building construction, high-efficiency equipment and appliance manufacturing, sales and installation, and management of energy use.