Smart thermostats have modified the way in which many individuals heat and funky their homes through the use of machine learning to reply to occupancy patterns and preferences, leading to a lower energy draw. This technology — which might collect and synthesize data — generally focuses on single-dwelling use, but what if this kind of artificial intelligence could dynamically manage the heating and cooling of a complete campus? That’s the concept behind a cross-departmental effort working to cut back campus energy use through AI constructing controls that respond in real-time to internal and external aspects.
Understanding the challenge
Heating and cooling may be an energy challenge for campuses like MIT, where existing constructing management systems (BMS) can’t respond quickly to internal aspects like occupancy fluctuations or external aspects resembling forecast weather or the carbon intensity of the grid. This leads to using more energy than needed to heat and funky spaces, often to sub-optimal levels. By engaging AI, researchers have begun to ascertain a framework to grasp and predict optimal temperature set points (the temperature at which a thermostat has been set to take care of) at the person room level and consider a bunch of things, allowing the present systems to heat and funky more efficiently, all without manual intervention.
“It’s not that different from what folks are doing in houses,” explains Les Norford, a professor of architecture at MIT, whose work in energy studies, controls, and ventilation connected him with the hassle. “Except now we have to take into consideration things like how long a classroom could also be utilized in a day, weather predictions, time needed to heat and funky a room, the effect of the warmth from the sun coming within the window, and the way the classroom round the corner might impact all of this.” These aspects are on the crux of the research and pilots that Norford and a team are focused on. That team includes Jeremy Gregory, executive director of the MIT Climate and Sustainability Consortium; Audun Botterud, principal research scientist for the Laboratory for Information and Decision Systems; Steve Lanou, project manager within the MIT Office of Sustainability (MITOS); Fran Selvaggio, Department of Facilities Senior Constructing Management Systems engineer; and Daisy Green and You Lin, each postdocs.
The group is organized around the decision to motion to “explore possibilities to employ artificial intelligence to cut back on-campus energy consumption” outlined in Fast Forward: MIT’s Climate Motion Plan for the Decade, but efforts extend back to 2019. “As we work to decarbonize our campus, we’re exploring all avenues,” says Vice President for Campus Services and Stewardship Joe Higgins, who originally pitched the concept to students on the 2019 MIT Energy Hack. “To me, it was a fantastic opportunity to utilize MIT expertise and see how we will apply it to our campus and share what we learn with the constructing industry.” Research into the concept kicked off on the event and continued with undergraduate and graduate student researchers running differential equations and managing pilots to check the bounds of the concept. Soon, Gregory, who can also be a MITOS faculty fellow, joined the project and helped discover other individuals to affix the team. “My role as a college fellow is to seek out opportunities to attach the research community at MIT with challenges MIT itself is facing — so this was an ideal fit for that,” Gregory says.
Early pilots of the project focused on testing thermostat set points in NW23, home to the Department of Facilities and Office of Campus Planning, but Norford quickly realized that classrooms provide many more variables to check, and the pilot was expanded to Constructing 66, a mixed-use constructing that’s home to classrooms, offices, and lab spaces. “We shifted our attention to check classrooms partly due to their complexity, but in addition the sheer scale — there are tons of of them on campus, so [they offer] more opportunities to collect data and determine parameters of what we’re testing,” says Norford.
Developing the technology
The work to develop smarter constructing controls starts with a physics-based model using differential equations to grasp how objects can heat up or cool down, store heat, and the way the warmth may flow across a constructing façade. External data like weather, carbon intensity of the facility grid, and classroom schedules are also inputs, with the AI responding to those conditions to deliver an optimal thermostat set point each hour — one that gives one of the best trade-off between the 2 objectives of thermal comfort of occupants and energy use. That set point then tells the present BMS how much to heat up or cool down an area. Real-life testing follows, surveying constructing occupants about their comfort. Botterud, whose research focuses on the interactions between engineering, economics, and policy in electricity markets, works to be certain that the AI algorithms can then translate this learning into energy and carbon emission savings.
Currently the pilots are focused on six classrooms inside Constructing 66, with the intent to maneuver onto lab spaces before expanding to your entire constructing. “The goal here is energy savings, but that’s not something we will fully assess until we complete a complete constructing,” explains Norford. “Now we have to work classroom by classroom to collect the info, but are taking a look at a much larger picture.” The research team used its data-driven simulations to estimate significant energy savings while maintaining thermal comfort within the six classrooms over two days, but further work is required to implement the controls and measure savings across a complete yr.
With significant savings estimated across individual classrooms, the energy savings derived from a complete constructing may very well be substantial, and AI can assist meet that goal, explains Botterud: “This whole concept of scalability is absolutely at the guts of what we’re doing. We’re spending quite a lot of time in Constructing 66 to determine how it really works and hoping that these algorithms may be scaled up with much less effort to other rooms and buildings so solutions we’re developing could make a big effect at MIT,” he says.
A part of that big impact involves operational staff, like Selvaggio, who’re essential in connecting the research to current operations and putting them into practice across campus. “Much of the BMS team’s work is finished within the pilot stage for a project like this,” he says. “We were capable of get these AI systems up and running with our existing BMS inside a matter of weeks, allowing the pilots to get off the bottom quickly.” Selvaggio says in preparation for the completion of the pilots, the BMS team has identified a further 50 buildings on campus where the technology can easily be installed in the long run to start out energy savings. The BMS team also collaborates with the constructing automation company, Schneider Electric, that has implemented the brand new control algorithms in Constructing 66 classrooms and is able to expand to latest pilot locations.
Expanding impact
The successful completion of those programs can even open the chance for even greater energy savings — bringing MIT closer to its decarbonization goals. “Beyond just energy savings, we will eventually turn our campus buildings right into a virtual energy network, where hundreds of thermostats are aggregated and coordinated to operate as a unified virtual entity,” explains Higgins. These kind of energy networks can speed up power sector decarbonization by decreasing the necessity for carbon-intensive power plants at peak times and allowing for more efficient power grid energy use.
As pilots proceed, they fulfill one other call to motion in Fast Forward — for campus to be a “test bed for change.” Says Gregory: “This project is a fantastic example of using our campus as a test bed — it brings in cutting-edge research to use to decarbonizing our own campus. It’s a fantastic project for its specific focus, but in addition for serving as a model for the way to utilize the campus as a living lab.”