“We came together to tackle the emissions generated by our built environment. It’s stated that the buildings that we work, live and play in account for over 38% of GHG emissions globally. Part of that comes from the construction of new buildings, but the majority of it comes from the daily operations of those buildings. They are by far the biggest energy consumers on our planet,” Ramadori said.
The goal is to use cloud technology to combine a building’s sensor-generated internal data with external sources of data – such as weather conditions, energy costs and if energy supply is generated using fossil fuels or cleaner sources – that impact a building’s energy use. The system can decide the optimal time to reduce or increase heating or cooling levels, taking into account factors like grid demand, energy tariffs and building occupancy levels, for example.
“All that richness of data can be brought together and then leveraged using the new capabilities that we have at our fingertips through artificial intelligence, which can both learn that building in a super granular fashion room by room, but then also make autonomous decisions that are much smarter using that data than a simple thermostat on the wall,” Ramadori said.
The technology is already at work, with AI-enabled cloud-based solutions being installed across parts of North America, including Canada’s largest mattress store, which has more than 200 locations spread across the country.
“The advent of the cloud allows us to deploy those stores in a way that’s really efficient and economical. And they’ve seen very material energy consumption savings of over 20%, both on electricity and gas.”
