Heating, ventilation and air-conditioning (HVAC) is the largest energy consumer in most buildings. Reaching net-zero — where on-site renewable generation balances annual consumption — requires both efficient equipment (heat pumps, thermal storage) and far smarter control. AI optimisation supplies the intelligence, cutting energy use while maintaining comfort.
Working principle
Traditional thermostats react after a room drifts off set-point. Model-Predictive Control (MPC) instead looks ahead: using a model of the building's thermal behaviour plus forecasts of weather, occupancy and electricity prices, it computes the control actions over a future horizon that minimise energy (or cost) subject to comfort constraints. Machine learning improves the building model from data and can pre-cool or pre-heat using thermal mass when renewable power is abundant or cheap.
| Aspect | Thermostat / rule-based | AI / MPC |
|---|---|---|
| Outlook | Reactive | Predictive (horizon) |
| Inputs | Current temperature | Weather, occupancy, price |
| Energy use | Baseline | Often 10–30% lower |
| Renewables | Ignored | Actively scheduled around |
Key ideaThermal mass becomes a battery: AI pre-conditions the building when solar output peaks or grid carbon is low, shifting load without sacrificing comfort.
Applications
- Net-zero offices and smart commercial buildings
- Grid-interactive efficient buildings (demand response)
- Campus and district energy optimisation
References & further reading
- Afram & Janabi-Sharifi, “Theory and applications of HVAC control systems — MPC review,” Building & Environment, 2014.
- Drgoňa et al., “All you need to know about model predictive control for buildings,” Annual Reviews in Control, 2020.
- IEA, “The Future of Heat Pumps,” 2022.