How AI Controls Heat Pumps in Large Buildings

Heat pumps are rapidly becoming the dominant heating and cooling technology in commercial buildings across Europe. Driven by decarbonization targets, rising gas prices and improving equipment economics, the transition from gas boilers to heat pumps is well underway, and accelerating.

But a heat pump is not a boiler. It behaves differently, responds differently to external conditions, and has characteristics that rule-based HVAC control systems are not well-equipped to handle. AI changes that and the combination of heat pump technology with AI-driven optimization is one of the most powerful tools available in commercial building energy management today.

How Heat Pumps Work in Commercial Buildings

A heat pump moves heat rather than generating it. In heating mode, it extracts heat energy from an external source, outdoor air, ground, or water, and transfers it into the building. In cooling mode, it reverses the process, extracting heat from the building and rejecting it externally.

The efficiency of a heat pump is expressed as its Coefficient of Performance (COP), the ratio of heat output to electrical input. A heat pump with a COP of 3 produces three units of heat for every unit of electricity consumed. At its best, a modern commercial heat pump can achieve COPs of 4 or higher.

The critical point is that COP is not fixed. It varies continuously with outdoor temperature, load conditions and equipment operating state. A heat pump running at partial load on a mild day operates very differently from one running at full capacity during a cold snap.

This variability is exactly what makes heat pumps both more efficient than gas boilers and more complex to control optimally.

Why Traditional Control Falls Short

Rule-based HVAC control was designed for simpler systems. A gas boiler delivers heat on demand at roughly constant efficiency, the control logic does not need to account for the fact that the boiler performs differently at different outdoor temperatures, because it largely does not.

Heat pumps are different. Their efficiency varies with conditions in ways that create real optimization opportunities, opportunities that fixed schedules and static setpoints cannot capture.

COP varies with outdoor temperature: A heat pump is most efficient when the difference between the outdoor temperature and the target flow temperature is smallest. On a mild day, efficiency is high. On the coldest days of winter, it drops. A static control system does not account for this, it simply calls for heat when the temperature drops below the setpoint, regardless of whether now is the most efficient time to run.

Thermal mass creates flexibility: Commercial buildings have significant thermal inertia, they heat up and cool down slowly. This means the building can be pre-heated or pre-cooled during periods of high heat pump efficiency or low electricity prices, storing that energy in the building fabric and releasing it during less favourable periods. A rule-based system cannot exploit this flexibility.

Defrost cycles affect performance: Air-source heat pumps periodically enter defrost cycles to clear ice from the outdoor unit. These cycles temporarily interrupt heating output and consume additional energy. AI systems can account for predicted defrost cycles in their control decisions, rule-based systems cannot.

Part-load operation is complex: Large commercial buildings typically have multiple heat pumps operating in parallel. The most efficient configuration, which units to run, at what capacity, in what sequence, depends on the current load, the outdoor conditions and the efficiency characteristics of each unit. This optimization is beyond what rule-based control can deliver.

How AI Optimizes Heat Pump Operation

AI-driven control systems like Myrspoven's myCoreAI address these challenges by continuously calculating the most efficient way to meet the building's heating and cooling needs given current and forecast conditions.

Weather-predictive pre-conditioning: By analyzing weather forecasts 24 to 48 hours ahead, the AI identifies windows when the heat pump will operate at maximum efficiency, typically mild periods when the outdoor temperature is closest to the target flow temperature, and pre-heats the building during those windows. This reduces the amount of heating required during colder, less efficient periods.

Dynamic flow temperature optimization: Heat pump efficiency improves when flow temperatures are lower. The AI continuously calculates the minimum flow temperature that will meet the building's comfort requirements given current conditions, reducing it when possible to maximize COP. This single intervention can deliver 5 to 10 percent efficiency improvement in heat pump systems that were previously operating at fixed high flow temperatures.

Load shifting to low-price electricity windows: Heat pumps are electrically driven, which means their operating cost is directly affected by electricity price variability. The AI analyses day-ahead spot electricity prices and shifts heat pump operation towards the cheapest periods, pre-heating the building when electricity is inexpensive and reducing operation during expensive peak hours. myLoadShift adds this capability on top of myCoreAI's efficiency optimization, and the combination can reduce electricity costs by up to 35 percent.

Multi-unit sequencing: In buildings with multiple heat pumps, the AI continuously calculates the most efficient combination of units to meet the current load, running fewer units at higher efficiency rather than all units at partial load, or staging units to optimize the aggregate COP of the system.

Integration with thermal storage: Buildings with thermal energy storage, hot or chilled water tanks, gain additional flexibility. The AI can charge storage during periods of high heat pump efficiency or low electricity prices and discharge it during periods when running the heat pump would be expensive or inefficient.

Real-World Performance

The performance gains from AI-optimized heat pump control are measurable and consistent.

Buildings that combine heat pump technology with AI optimization typically achieve:

• 20 to 30 percent reduction in heat pump electricity consumption compared to the same equipment on rule-based control
• Up to 35 percent reduction in electricity costs when load shifting is added
• Improved comfort consistency more stable temperatures across zones, fewer extremes during cold snaps
• Longer equipment life more efficient operation reduces thermal cycling and peak load stress on compressors

The gains are largest in buildings where the existing control system was least optimized, those running heat pumps on fixed flow temperatures, fixed schedules or without any weather compensation.

Practical Considerations for Large Buildings

Large commercial buildings, offices, retail centers, logistics facilities, have additional considerations that affect how AI heat pump control is implemented.

Multiple heat pump configurations: Large buildings often combine different heat pump types, air-source units for different zones, ground source for base load, reversible units for heating and cooling. AI control needs to optimize across the whole system, accounting for the interaction between units and the thermal zones they serve.

Integration with existing BMS: As with other AI building control applications, integration with the existing building management system is the critical implementation step. The AI needs to read data from temperature sensors, flow meters and equipment status monitors throughout the system, and write setpoints back to the BMS control logic. Standard protocols (BACnet, Modbus) handle this in most modern buildings.

Commissioning and learning period: AI optimization improves as it learns the specific characteristics of a building's heat pump system. Expect a commissioning period of four to eight weeks during which the system establishes baselines, calibrates its models and begins delivering optimized control. Performance typically improves further over the first six months as the AI accumulates more operational data.

Fallback and safety: The AI control layer should always operate with the existing BMS as a fallback. If connectivity is lost or the AI system is unavailable, the BMS reverts to its previous control logic automatically. Safety functions, frost protection, high pressure cutouts, overtemperature limits, remain in the BMS and are not modified by the AI layer.

The Electrification Opportunity

For buildings still operating gas boilers, the combination of heat pump installation and AI optimization represents the highest-impact energy efficiency and decarbonization step available.

Heat pumps powered by renewable electricity produce a fraction of the emissions of gas heating. AI optimization maximizes their efficiency and minimizes their running cost. The two technologies are complementary and the financial case for the combination, particularly in markets with high gas prices and volatile electricity, has never been stronger.

For buildings already operating heat pumps, AI optimization captures the efficiency gains that fixed-schedule control leaves on the table. In most cases, the investment pays back within 12 to 24 months.

The Bottom Line

Heat pumps are more efficient than gas boilers, but only if they are controlled intelligently. The variable, weather-dependent, load-sensitive nature of heat pump operation is exactly what AI is designed to handle.

The combination of modern heat pump technology and AI-driven optimization delivers results that neither can achieve alone, and positions buildings for both immediate cost savings and long-term regulatory compliance.

Want to understand what AI heat pump optimization would deliver in your buildings? Talk to our team.