Heating and lighting buildings requires a vast amount of energy: 18% of all global energy consumption, according to the International Energy Agency. Contributing to the problem is the fact that many buildings’ HVAC systems are outdated and slow to respond to weather changes, which can lead to severe energy waste.
Some scientists and technologists are hoping that AI can solve that problem. At the moment, much attention has been drawn to the energy-intensive nature of AI itself: Microsoft, for instance, acknowledged that its AI development has imperiled their climate goals. But some experts argue that AI can also be part of the solution by helping make large buildings more energy-efficient. One 2024 study estimates that AI could help buildings reduce their energy consumption and carbon emissions by at least 8%. And early efforts to modernize HVAC systems with AI have shown encouraging results.
“To date, we mostly use AI for our convenience, or for work,” says Nan Zhou, a co-author of the study and senior scientist at the Lawrence Berkeley National Laboratory. “But I think AI has so much more potential in making buildings more efficient and low-carbon.”
AI in Downtown Manhattan
One example of AI in action is 45 Broadway, a 32-story office building in downtown Manhattan built in 1983. For years, the building’s temperature ran on basic thermostats, which could result in inefficiencies or energy waste, says Avi Schron, the executive vice president at Cammeby’s International, which owns the building. “There was no advance thought to it, no logic, no connectivity to what the weather was going to be,” Schron says.
In 2019, New York City enacted Local Law 97, which set strict mandates for the greenhouse emissions of office buildings. To comply, Schron commissioned an AI system from the startup BrainBox AI, which takes live readings from sensors on buildings—including temperature, humidity, sun angle, wind speed, and occupancy patterns—and then makes real-time decisions about how those buildings’ temperature should be modulated.
Sam Ramadori, the CEO of BrainBox AI, says that large buildings typically have thousands of pieces of HVAC equipment, all of which have to work in tandem. With his company’s technology, he says: “I know the future, and so every five minutes, I send back thousands of instructions to every little pump, fan, motor and damper throughout the building to address that future using less energy and making it more comfortable.” For instance, the AI system at 45 Broadway begins gradually warming the building if it forecasts a cold front arriving in a couple hours. If perimeter heat sensors notice that the sun has started beaming down on one side of the building, it will close heat valves in those areas.
After 11 months of using BrainBox AI, Cammeby has reported that the building reduced its HVAC-related energy consumption by 15.8%, saving over $42,000 and mitigating 37 metric tons of carbon dioxide equivalent. Schron says tenants are more comfortable because the HVAC responds proactively to temperature changes, and that installation was simple because it only required software integration. “It’s found money, and it helps the environment. And the best part is it was not a huge lift to install,” Schron says.
Read More: How AI Is Fueling a Boom in Data Centers and Energy Demand
BrainBox’s autonomous AI system now controls HVACs in 4,000 buildings across the world, from mom-and-pop convenience stores to Dollar Trees to airports. The company also created a generative AI-powered assistant called Aria, which allows building facility managers to control HVACs via text or voice. The company expects Aria to be widely available in early 2025.
Scientific Studies
Several scientists also see the potential of efforts in this space. At the Lawrence Berkeley National Laboratory in California, Zhou and her colleagues started studying the potential impacts of AI on building efficiency several years before ChatGPT captured public attention. This year, they published a paper arguing that AI/HVAC integration could lead to a 8 to 19% decrease in both energy consumption and carbon emissions—or an even bigger decrease if paired with aggressive policy measures. AI, the paper argues, might help reduce a building’s carbon footprint at every stage of its life cycle, from design to construction to operation to maintenance. It could predict when HVAC components might fail, potentially reducing downtime and costly repairs.
Zhou also argues that AI systems in many buildings could help regional electricity grids become more resilient. Increasingly popular renewable energy sources like wind and solar often produce uneven power supplies, creating peaks and valleys. “That’s where these buildings can really help by shifting or shedding energy, or responding to price signals,” she says. This would help, for instance, take pressure off the grid during moments of surging demand.
Other efforts around the world have also proved encouraging. In Stockholm, one company implemented AI tools into 87 HVAC systems in educational facilities, adjusting temperature and airflow every 15 minutes. These systems led to an annual reduction of 64 tons of carbon dioxide equivalent, a study found, and an 8% decrease in electricity usage. And the University of Maryland’s Center for Environmental Energy Engineering just published a study arguing that AI models’ predictive abilities could significantly reduce the power consumption of complex HVAC systems, particularly those with both indoors and outdoor units.
As the globe warms, efficient cooling systems will be increasingly important. Arash Zarmehr, a building performance consultant at the engineering firm WSP, says that implementing AI is a “necessary move for all designers and engineers.” “All engineers are aware that human controls on HVAC systems reduce efficiencies,” he says. “AI can help us move toward the actual decarbonization of buildings.”
Despite its potential, AI’s usage in building efficiency faces challenges, including ensuring safety and tenant data privacy. Then there’s the larger question of AI’s overall impact on the environment. Some critics accuse the AI industry of touting projects like this one as a way to greenwash its vast energy usage. AI is driving a massive increase in data center electricity demand, which could double from 2022 to 2026, the International Energy Agency predicts. And this week, University of California Riverside and Caltech scientists published a study arguing that the air pollution from AI power plants and backup generators could result in 1,300 premature deaths a year in the U.S. by 2030. “If you have family members with asthma or other health conditions, the air pollution from these data centers could be affecting them right now,” Shaolei Ren, a co-author of the study, said in a statement. “It’s a public health issue we need to address urgently.”
Zhou acknowledges that the energy usage of AI data centers “increased drastically” after she and her colleagues started writing the paper. “To what extent it will offset the emission reduction we came up with in our paper needs future research,” she says. “But without doing any research, I still think AI has much more benefits for us.”
