James Darley writes on the AI magazine website about a new report by KPMG the multinational professional services network: How AI is Helping to Improve Energy Efficiency and Management in Real Estate.
KPMG: AI Systems Can Cut Building Energy Waste By Up To 30%
When it comes to preventing carbon emissions, there are two main schools of thought.
The first of those is all about changing the forms of energy we use, from fossil fuels to renewables.
The second is about reducing the amount of energy we use altogether by improving the efficiency of technologies, buildings and infrastructure.
The latter approach is often easier and more cost effective than the former, as it focuses on minimising waste rather than relying on expensive upgrades to hardware.
According to new research recently published by KPMG, the latter approach can be helped along by AI, especially with regards to the energy efficiency of buildings.
In its new report, titled How AI is Helping to Improve Energy Efficiency and Management in Real Estate, KPMG suggests that traditional retrofits – such as swapping a boiler for a heat pump or changing the insulation in your home – will likely be insufficient if we want to meet our global 2050 net zero targets.
Instead, the consultancy advocates for Strategic Energy Management (SEM) frameworks powered by AI systems.
These frameworks can be integrated into the heating systems and electricity networks of buildings through the Internet of Things (IoT), automatically communicating when and when not to use energy.
The real-world applications
Companies implementing AI-based energy management are already seeing substantial reductions in consumption.
Donatas Karčiauskas, CEO of commercial building energy efficiency firm Exergio, confirms the findings align with field experience.
“AI is already helping buildings cut waste by 20-30% in our projects, no matter the climate or the age of the property,” Donatas says.
“But those savings only last if there’s smart energy management behind them.”
The CEO emphasises that success depends on ongoing operational oversight rather than one-time system installations.
A three-tier approach
KPMG’s research outlines a hierarchical approach to energy efficiency improvements.
The first tier focuses on optimising existing systems, with AI automatically adjusting HVAC, lighting and control parameters based on real-time conditions.
According to Donatas, this is “a task of AI at the moment as we want to achieve faster savings”.
The second tier involves replacing outdated equipment such as boilers, chillers and pumps with more efficient models.
Renewable energy installations and long-term power contracts form the third tier, but only after baseline consumption has been optimised.
The study stresses that renewables deliver limited value without prior consumption management.
How does a ‘strategic energy management framework work?
SEM alone typically delivers 5% to 7% annual savings, but when combined with AI systems, efficiency gains increase to 20-30%.
The framework operates through a five-step cycle encompassing assessment, planning, implementation, capability building and monitoring.
Within this structure, AI systems can regulate HVAC systems based on occupancy patterns, weather data and usage metrics while facility managers define energy targets and comfort parameters.
Donatas describes the approach as creating “a culture of active energy management” where “SEM lays down the rules, and AI keeps the systems running to them minute by minute, with people still in control”.
The importance of transparency
The research emphasises the importance of maintaining human oversight within AI-powered energy management systems.
Donatas notes that his company’s platform “connects to the building’s energy management systems and uses metrics such as sensor data and occupancy patterns to adjust HVAC simultaneously”.
This approach ensures “efficiency becomes a continuous management task, not something postponed until the next renovation”.
The study advocates for what KPMG terms “human-centric AI” that maintains transparency and builds user trust while delivering automated optimisation.
The findings suggest that energy efficiency improvements depend more on management practices than technological hardware upgrades, potentially offering a faster path to emissions reductions than traditional retrofit programmes.
External link
Energy in Demand - Sustainable Energy - Rod Janssen 