Imagine a building that doesn’t just turn systems on and off, but thinks ahead like a seasoned operator. Instead of reacting after rooms get too hot or too cold, the AI reads the pulse of the building—temperature sensors, HVAC signals, and even renewable inputs like geothermal or solar.
Every second, it translates this flow of data into real-time decisions:
Should cooling be reduced in one wing because sunlight has already warmed it?
Is it more efficient to pre-heat a space before peak hours rather than running the system at full force later?
Can renewable energy be prioritized while still keeping people comfortable?
This is what we call Real-Time Multivariable Control. It’s not a fixed program, but an intelligent layer that constantly adapts. By predicting building load profiles, it anticipates changes before they happen—avoiding wasted energy while keeping conditions stable.
For the people inside, the experience is simple: comfort feels effortless. For the operators, the benefit is profound: reduced energy use, lower costs, and a system that learns and improves day by day.
Across cities, public institutions face a growing challenge: balancing comfort for citizens while reducing operating costs and meeting carbon neutrality goals. Traditional building management systems (BMS) often require costly equipment replacement and lengthy integration projects, making them inaccessible for many municipalities.
Recently, our AI-based energy optimization technology was integrated into the daily operations of a public institution building. Unlike conventional approaches, this deployment required no replacement of existing HVAC or mechanical systems. Instead, our solution connected in parallel with the building’s current infrastructure, enabling intelligent control without disrupting ongoing operations.
How It Works in Practice
Seamless Integration: The AI module was connected to the central heating, cooling, and ventilation systems through existing control points. No invasive construction or downtime was required.
Real-Time Multivariable Control: By collecting data from sensors, HVAC units, and renewable systems, the AI continuously generates optimal operation commands. It predicts building load profiles, balances comfort levels, and minimizes wasted energy.
Shadow Simulation for Reliability: Before activating direct control, the AI runs a parallel “shadow” simulation to test outcomes in real-time. This ensures that changes remain within the defined comfort range of 95% occupant satisfaction, a level already validated in prior pilots.
In one public building where our AI optimization was deployed, the transformation became evident within just a few months.
Energy Use Down by Double Digits:
Carbon Footprint Shrinking in Parallel:
Operational Budgets Benefiting Too:
Comfort Maintained, Even Enhanced:
This is more than a single case—it is a glimpse into the future of public buildings everywhere: spaces that deliver comfort, cut costs, and take measurable steps toward carbon neutrality, all at the same time.
