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Lee’s research applies AI solutions to building science to tackle this issue. In the first year of the project the team is focused on creating a digital twin – or virtual representation – of the Exam Centre at 255 McCaul St.
In the next stage, the researchers will develop a deep reinforcement learning algorithm for the optimal control of the heating and cooling systems. This algorithm will be pre-trained with the digital twin to avoid putting excessive stress on the actual building.
The algorithm will then be implemented in the real Exam Centre and further fine-tuned through interactions with the building. If successful, Lee hopes to use the same approach to convert more campus buildings to smart buildings, contributing to U of T’s Low-Carbon Action Plan.
“60 percent of campus energy consumption on the St. George campus comes from heating and cooling buildings,” he says.
Lee’s research group is also investigating how humans interact with their buildings in an NSERC Discovery-funded project on scalable systems for intelligent and interactive buildings, an emerging area of study with relatively little published research – something Lee hopes to change.
Where previous methods relied on data such as the correlation between thermostat setpoint temperature and other parameters (such as the time of the day), Lee and his team are instead using causal relations – for example, the factors affecting occupants’ decision-making on thermostat setpoint temperature – to develop reliable human-centric smart solutions.
“Once we understand how humans interact with their buildings in the light of causation, we can realize more intelligent and human-interactive buildings,” says Lee.
While Lee is not the only researcher interested in using machine learning and AI techniques in buildings, the sector has lagged behind other sectors such as the automotive or health-care industries, because of how different the energy consumption profiles and needs of individual buildings can be.
“A solution customized for one building is not necessarily fully transferable to another,” Lee says.
“This is a major roadblock in the path of making our buildings smarter. If we can seamlessly combine existing building-science domain knowledge and AI, we can build scalable and reliable solutions to create sustainable buildings.”
To tackle this issue, the team is partnering with PLC Group, along with funding from the Ontario Centre of Innovation, to develop a scalable digital twinning tool for building energy systems. If the tool is effective, it will equip the building industry with a solution to create intelligent, interactive and more sustainable buildings around the world.
“The use of AI in building management systems not only has the potential to improve the sustainability of our built environment significantly, but also how we interact with it,” Lee says.
Source: University of Toronto
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