KPMG: What Role Can AI Play in Boosting Building Efficiency?
An ongoing challenge in curbing carbon emissions is the decision between adopting renewable energy sources and enhancing existing building efficiency.
While switching to renewable energy is a viable option, reducing existing energy use by enhancing technology, buildings and infrastructure can be more feasible and affordable.
Recent research by KPMG supports the latter approach, highlighting how AI can enhance the energy efficiency of buildings.
KPMG's study, 'How AI is Helping to Improve Energy Efficiency and Management in Real Estate', suggests that conventional retrofits like changing boilers or improving insulation might not suffice for reaching 2050 net-zero targets globally.
Hence, adopting AI-powered Strategic Energy Management (SEM) frameworks is advocated.
These frameworks, run and delivered through the Internet of Things (IoT), integrate into heating and electrical networks to intelligently regulate energy consumption.
The real-world applications
Companies incorporating AI-driven energy management are witnessing notable reductions in energy use.
"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."
A three-tier approach
The KPMG research proposes a tiered method for enhancing energy efficiency.
The approach starts by optimising current systems, where AI adjusts HVAC, lighting and control settings based on live conditions to achieve expedited savings.
The second tier recommends replacing old equipment with efficient versions. Lastly, after optimising baseline consumption, the focus shifts to renewable installations and long-term power agreements, with the research noting limited benefits from renewables without prior consumption control.
How does a strategic energy management framework work?
SEM frameworks alone can yield 5-7% annual savings, but with AI integration, efficiency could rise to 20-30%.
These frameworks incorporate a five-step process: assessment, planning, implementation, capability building and monitoring.
AI systems support managing HVAC operations based on occupancy, weather forecasts and usage stats, whereas facility managers establish energy benchmarks and comfort standards.
Donatas describes 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."
AI is already helping buildings cut waste by 20% to 30% in our projects, no matter the climate or the age of the property.
The importance of transparency
Human oversight remains crucial in AI-supported energy management, as emphasised by KPMG.
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."
This method ensures energy efficiency is a constant management focus, instead of being deferred to future renovations.
The study advocates a "human-centric AI" philosophy that upholds transparency and user trust while automating optimisation efforts.
Findings indicate that management practices can achieve energy efficiency advancements quickly than tech hardware updates alone, providing a quicker pathway to emission reductions than traditional retrofit methods.
