AI-enabled building systems could cut whole-building energy use by up to 22% compared with traditional controls, according to new research from Schneider Electric.
The study found that using artificial intelligence to optimise heating, ventilation and air conditioning (HVAC) could deliver an additional 7.2% to 12.7% in energy savings beyond conventional digital building controls.
The research examined building scenarios in Australia, India and the US, with some scenarios recording more than 200 MWh of annual energy savings and up to 59,869kg of avoided CO₂e emissions.
Schneider Electric said the research used building-energy modelling validated against real-world pilot deployments. The figures therefore represent potential savings under the scenarios studied rather than a guaranteed reduction for every building.
AI Could Make HVAC Systems More Responsive
The research focuses on putting an AI layer over existing digital building management systems rather than replacing heating and cooling equipment.
The system can bring together information that is often held separately, including occupancy patterns, weather forecasts, equipment performance and other building data.
AI can then analyse those inputs continuously and adjust HVAC operation as conditions change.
Pankaj Sharma, EVP of Software & Services at Schneider Electric, said better use of building data will be central to the next stage of building management.
“The future of building management will be defined by how effectively organisations connect and contextualise data that was previously trapped in silos.”
He said an AI layer could turn that information into actions that reduce energy use and operating costs.
“An AI layer on top of existing business systems can turn complexity into intelligence and intelligence into action, reducing emissions, lowering costs and improving performance simultaneously.”
The research found that AI-enabled optimisation could more than double the energy savings achieved by smart building controls operating without the AI layer.
Australian Buildings Were Included In The Study
Australia was one of the three markets examined in the research, alongside India and the US.
That makes the findings relevant to Australian commercial buildings, although Schneider Electric’s modelling does not establish that Australian buildings will achieve a particular percentage reduction in energy use.
Instead, the Australian scenarios form part of a wider analysis of how AI-enabled HVAC control performs under different building types and operating conditions.
The distinction matters because actual energy savings can vary according to a building’s existing equipment, controls, occupancy patterns and operating schedule.
The research also comes as electricity demand from buildings and other sectors continues to grow, increasing the focus on technologies that can reduce consumption as well as improve how electricity is managed.
Smaller Buildings Could Benefit From AI
Schneider Electric also identified a potential opportunity for smaller commercial buildings.
The research examined buildings below 100,000 square feet, where energy management systems can be harder to adopt because of cost, complexity and limited access to specialist facilities expertise.
AI could automate some of the optimisation work, potentially allowing smaller operators to use more advanced energy management without requiring the same level of dedicated expertise.
Sharma said this could broaden access to energy-management technology beyond large commercial estates.
“AI is putting the power of energy intelligence into the hands of smaller building owners and operators.”
He said processes that previously required significant expertise and investment could become easier to implement at scale.
The research examined both cloud and edge AI deployments and found potential energy and carbon benefits from both approaches.
The 22% Figure Is A Modelled Result
The headline 22% reduction should not be interpreted as a guaranteed saving on a building’s electricity bill.
Schneider Electric’s study used energy modelling that was validated against real-world pilot deployments. It assessed different building scenarios rather than measuring a uniform group of buildings before and after AI installation.
The research also estimated annual utility savings of between US$13,600 and US$49,300 per building at the commercial energy rates used in the study.
Those financial figures are similarly dependent on the building and energy conditions being modelled.
For Australian building owners, the more immediate significance is the potential to make existing HVAC systems more responsive to the conditions around them.
Rather than relying solely on fixed schedules and programmed settings, AI-enabled controls can continuously assess available data and adjust operation as occupancy, weather and equipment conditions change.
Sharma said rising energy demand would make the ability to manage these pressures together increasingly important.
“As energy demand continues to rise, the ability to deliver these outcomes together, rather than forcing organisations to choose between them, will be critical to achieving both business and sustainability goals.”
The findings suggest AI could become another tool for reducing energy waste in buildings, particularly as smaller operators gain access to systems that were previously more difficult to manage.
