MQ Flow runs a building's heating, ventilation and humidity from a physics model of human comfort, then learns what the people in each room actually prefer. Occupants never set a temperature. They tap warmer or cooler, and the system does the rest.

We build the AI and the software. Our HVAC partner Mifimi brings the domain knowledge that makes it work in a plant room. This is a work note, not a case study: the building has not yet produced the numbers a case study needs.

The problem

A thermostat controls air temperature, but people feel radiant heat, humidity and air movement as well. So buildings run warmer or colder than anyone needs, and the heat pump does work that fresh air or a humidifier could have done for less. MQ Flow starts from what people feel, not from a setpoint.

How it works

  • Baseline: Physics first. A Fanger PMV comfort model (ISO 7730) computes each room's comfort target from temperature, radiant heat, humidity, and air movement. It works from day one, before any learning.
  • Personalise: Learns each room. Per-room reinforcement learning turns warmer and cooler taps into a personal comfort offset, tuning to the actual people in the room rather than a statistical average.
  • Optimise: Cheapest levers first. The same comfort can be reached several ways. The optimiser adjusts fresh air and humidity before touching the heat pump, targeting at least 30% lower energy use.
The MQ Flow eco meter in its green good state, reporting good energy management, beside activity level and clothing type controls. The MQ Flow eco meter in its yellow-green average state, telling the room it can improve efficiency. The MQ Flow eco meter in its amber poor state, warning that too many corrections were made and asking the room to adapt to the temperature. The MQ Flow chart view in dark theme, plotting indoor against outdoor temperature across the day. The same MQ Flow chart view in light theme, showing the interface adapts to either theme. MQ Flow room settings: room type and orientation, window size and glazing, HVAC temperature limits, and seasonal preferences.
The interface up close: the eco meter coaching the room from good to poor, indoor against outdoor temperature in either theme, and the room setup behind it. Occupants see activity and clothing, never a setpoint.
Two and a half minutes of MQ Flow running: live conditions, occupant feedback, the eco meter responding, and the settings behind it.

What we chose, and why

  • Comfort is computed, not guessed: PMV weighs radiant heat, humidity and air movement, not air temperature alone.
  • Occupant taps are the training signal for the reinforcement learning, so the system improves with use.
  • Built to drop into existing buildings: BACnet integration and one API toward any PLC.
  • The control loop runs on hardware in the building, per-room thermostat units on our edge stack plus a weather station, so it keeps working without a cloud connection.

Where it stands

The first batch of 28 thermostat units is going into a faculty building in Belgrade, where data collection and model use are being evaluated on the live building.

Verified numbers

None yet. At least 30% lower energy use is the project target, not a result. We publish measured figures when the evaluation closes, and not before.

Mifimi is named with written permission. MQ Flow is looking for design partners with a building to instrument; if that is you, book a call.