Buildings account for about 40 percent of U.S. energy consumption, and are responsible for one-third of global carbon dioxide emissions. Making buildings more energy-efficient is not only a cost-saving measure, but a crucial climate change mitigation strategy. Hence the rise of “smart” buildings, which are increasingly becoming the norm around the world.
Smart buildings automate systems like heating, ventilation, and air conditioning (HVAC); lighting; electricity; and security. Automation requires sensory data, such as indoor and outdoor temperature and humidity, carbon dioxide concentration, and occupancy status. Smart buildings leverage data in a combination of technologies that can make them more energy-efficient.
Since HVAC systems account for nearly half of a building’s energy use, smart buildings use smart thermostats, which automate HVAC controls and can learn the temperature preferences of a building’s occupants.
In a paper published in the journal Applied Energy, researchers from the MIT Laboratory for Information and Decision Systems (LIDS), in collaboration with Skoltech scientists, have designed a new smart thermostat which uses data-efficient algorithms that can learn optimal temperature thresholds within a week.
“Despite recent advances in internet-of-things technology and data analytics, implementation of smart buildings is impeded by the time-consuming process of data acquisition in buildings,” says co-author Munther Dahleh, professor of electrical engineering and computer science and director of the Institute for Data, Systems, and Society (IDSS). Smart thermostat algorithms use building data to learn how to operate optimally, but the data can take months to collect.
To speed up the learning process, the researchers used a method called manifold learning, where complex and “high-dimensional” functions are represented by simpler and lower-dimensional functions called “manifolds.” By leveraging manifold learning and knowledge of building thermodynamics, the researchers replaced a generic control method, which can have many parameters, with a set of “threshold” policies that each have fewer, more interpretable parameters. Algorithms developed to learn optimal manifolds require fewer data, so they are more data-efficient.
The algorithms developed for the thermostat employ a methodology called reinforcement learning (RL), a data-driven sequential decision-making and control approach that has gained much attention in recent years for mastering games like backgammon and Go.
“We have efficient simulation engines for computer games that can generate abundant data for the RL algorithms to learn a good playing strategy,” says Ashkan Haji Hosseinloo, a postdoc at LIDS and the lead author of the paper. “However, we do not have the l
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