Commercial buildings HVAC AI.
AI-driven HVAC coordination optimising energy consumption while maintaining comfort across large commercial buildings — substantial savings, real sustainability gains.
- Client
- mCloud Technologies
- Industry
- Commercial real estate
- Services
- AI / ML · IoT · building automation
Platform · HVAC AIHVAC eats power. Comfort suffers. Both at once.
Operating large commercial buildings is expensive — especially in high-cost electricity states like New York and California. HVAC, particularly air conditioning, consumes substantial energy. The core difficulty: balancing occupant comfort with the imperative to reduce electrical usage.
Traditional, disparate systems lead to inefficient AC modulation — over-cooling some zones, under-cooling others, wasting energy with reactive adjustments, and generating tenant satisfaction issues that hurt retention. Sustainability goals slip. Bills climb.
mCloud Technologies brought this problem at portfolio scale: dozens of commercial properties, hundreds of zones, and a sustainability mandate that made every kilowatt count. Across that footprint, even a few points of HVAC waste added up to roughly six figures a year.
Build an AI that thinks about every zone, every minute.
We partnered with mCloud Technologies to develop and deploy an AI-driven coordination system for HVAC across commercial properties. It integrates seamlessly with existing environmental sensors, occupancy data, and individual AC unit control systems. ML algorithms analyse real-time data to predict cooling needs, automate temperature adjustments across zones, and orchestrate AC unit cycles intelligently.
The platform streams from IoT sensors (temperature, humidity, CO2, occupancy, pressure and flow) over BACnet and Modbus into models trained on TensorFlow and PyTorch, running on Google Cloud. Anomaly detection flags failing equipment early, and real-time control loops push setpoints back to each AC unit, so the system tunes itself zone by zone without an operator in the loop.
Delivery started with instrumentation. We mapped each building's zones, added sensors where the existing building-management system had blind spots, and normalised the data coming over BACnet and Modbus into one stream the models could learn from. Only once the data was trustworthy did we let the AI take the wheel on setpoints, which kept the early rollout safe and the facilities team comfortable.
The models train on each property's own patterns: occupancy rhythms, weather, tariff windows, and how quickly each space heats and cools. Rather than a single global rule, the system makes a per-zone decision every few minutes, pre-cooling ahead of a price spike here, easing off an empty floor there, and holding occupied spaces inside their comfort band the whole time.
Because it plugs into the existing control hardware instead of replacing it, the rollout was low risk and quick to widen. Facilities teams kept their dashboards and manual overrides; the AI simply took the constant tuning off their plate and ran it continuously, building by building, across the portfolio.
In practice the savings compound quietly. Lower peak demand, fewer after-hours service calls, and equipment that runs within spec instead of fighting itself all add up across a portfolio, which is why an optimisation that started as an energy play also paid back in maintenance and tenant retention.
“The AI-driven HVAC optimisation delivered real energy and cost savings across our portfolio, and occupant comfort actually went up, which we didn't expect to get at the same time.”
VP of Asset Optimization, mCloud Technologies
What actually shipped.
25% energy reduction
Reduced HVAC energy consumption by 25% through intelligent scheduling and dynamic temperature setpoints, measured across the pilot portfolio.
20% cost savings
Up to 20% decrease in electricity costs — substantial savings, especially in high-tariff regions like NY and CA.
15% comfort improvement
Increased occupant thermal comfort satisfaction by 15% via proactive, personalised climate control.
Extended equipment life
Optimised AC run cycles to reduce unnecessary wear and tear, extending unit lifespan significantly.
The tools we shipped with.
IoT-first architecture connecting sensors, ML inference, and existing building systems.
25% less energy. 20% lower bills. Same comfort.
mCloud Technologies achieved 25% energy reduction and 20% lower operating costs. See what AI-driven optimisation can do for your facility.