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More efficient equipment helps with energy reduction strategies, but AI and a strategic energy management framework are more effective in delivering results.
Outdated hardware isn’t the issue with efforts to maximize efficiency and reduce energy use, experts reveal. AI can help with strategic energy management. (Image courtesy of Exergio.)
Traditional retrofits are too slow and costly to deliver the scale of cuts required to reach net-zero goals of 2050, and buildings waste energy even after costly upgrades, but paths to better results do exist, and Artificial Intelligence (AI) can help, suggests a recent KPMG study on energy efficiency and management in buildings.
The company’s How AI is helping to improve energy efficiency and management in real estate report suggests the solution lies in Strategic Energy Management (SEM). SEM is a model that turns energy and emissions from long-term goals into day-to-day tasks backed by AI to achieve higher efficiency rates.
Exergio, a company that developed an AI-based tool for energy efficiency in commercial buildings, says that findings reflect what is already visible in real-world cases.
“AI is already helping buildings cut waste by 20 to 30 per cent in our projects, no matter the climate or the age of the property,” said Exergio CEO Donatas Karčiauskas. “But those savings only last if there’s smart energy management behind them. That’s exactly the point KPMG makes, efficiency isn’t a one-off upgrade, it’s how you run the building day after day.”
The report from KPMG explains that SEM should track how buildings use energy and assign clear responsibility for fixing problems. According to Karčiauskas, this usually means facility managers or energy officers are tasked with day-to-day oversight. However, certain tasks such as changing parameters in sensors should be automatically assigned to AI and machine learning models to adjust in real-time, with experts overseeing the process, he says.
According to information provided by Exergio, implementing an SEM on its own typically only delivers five to seven per cent savings per year, but when used with AI, the savings rise to around 20 to 30 per cent.
There are three tiers of SEM, according to the report. The first tier focuses on getting more out of what is already in place. Engineers have to tune HVAC, lighting and control systems so they run more efficiently day to day. This, according to Karčiauskas, is “a task of AI at the moment as we want to achieve faster savings.”
The second step is replacing worn or outdated equipment with models that use less energy. The third adds renewables or long-term power contracts, but only once the building’s basic energy consumption has been brought under control.
The authors of the paper stress that renewables should come last, since they deliver limited value if the building’s consumption has not already been optimized.
The study also indicated that efficiency depends less on new hardware and more on how existing systems are managed.
“What’s missing is a culture of active energy management. SEM lays down the rules, and AI keeps the systems running to them minute by minute, with people still in control,” continued Karčiauskas.
SEM has a five-step cycle. It includes assessment, planning, implementation, building capability and monitoring. Within this setup, he pointed to an example where AI could regulate HVAC use concurrently based on occupancy, weather and usage, while managers define energy-saving goals, set comfort ranges and review results.
“We used the same approach before it was called SEM, simply because it made sense,” explained Karčiauskas. “Our platform connects to the building’s energy management systems and uses metrics such as sensor data and occupancy patterns to adjust HVAC simultaneously. That’s how efficiency becomes a continuous management task, not something postponed until the next renovation.”