Cohort: 1 

Location: Munich, Germany 

Application Title: AI-based closed-loop HVAC automation for building performance and energy efficiency 

Team Members: Rahul Chillar (executive sponsor), Alexander Gloning, Carlos-Eduardo Pazos-Zarain, Monica Arnaudo, Paul Baumann

The challenge 

Managing large commercial buildings is complex. Heating, ventilation and air conditioning systems must balance energy use with occupant comfort across multiple floors and rooms. Conditions change constantly as people move through buildings and weather shifts throughout the day. 

Traditional building management systems rely on fixed schedules and manual adjustments. Facility managers monitor dashboards and respond when problems arise, often after occupants report discomfort or energy use exceeds expectations. These systems usually operate in silos and lack the ability to adapt continuously to real-time conditions. 

Maintaining consistent thermal comfort is particularly difficult. Fixed HVAC settings cannot respond quickly to changes in occupancy or external temperature, leading to uneven indoor conditions and unnecessary energy consumption. As buildings become larger and more dynamic, these limitations become more pronounced. 

The solution 

An AI-based closed-loop HVAC automation built on existing infrastructure 

Siemens developed an AI-based closed-loop HVAC automation that optimises building performance in real time without requiring new hardware. 

The system connects to existing HVAC equipment and sensors, using live operational data to guide control decisions. Rather than replacing building management systems, it sits alongside them and provides an additional layer of intelligence focused on comfort and efficiency. 

The objective is to support facility managers by automating routine adjustments while maintaining transparency and control. 

How it works 

Closed-loop optimisation of indoor conditions 

The AI system analyses incoming data from building systems and combines it with short-term forecasts. It learns how different areas of a building respond to heating and cooling actions and adjusts settings accordingly. 

Decisions are made continuously rather than on fixed schedules. HVAC setpoints are updated as occupancy changes or external conditions shift, helping maintain comfort while avoiding unnecessary energy use. The system operates as a closed loop, monitoring outcomes and refining its behaviour over time. 

Because it runs on an edge–cloud architecture, the system can respond quickly at the building level while benefiting from centralised analytics. 

How it differs from conventional approaches 

Moving beyond static HVAC control 

Conventional building management systems apply predefined rules that change infrequently and depend on manual intervention. Adjustments are reactive and often delayed. 

Siemens’ approach uses AI to anticipate changes and adjust HVAC operation proactively. By continuously learning from building utilization, the system reduces the need for manual tuning and supports more consistent comfort across spaces. 

Importantly, this is achieved without replacing existing infrastructure, which lowers the barrier to deployment. 

Impact 

• 28% increase in comfort compliance in pilot deployments 
• 6.4% reduction in monthly energy costs at pilot sites 

Why it matters 

Improving comfort while reducing energy use 

As buildings account for a large share of energy consumption, improving operational efficiency without disrupting occupants is an important challenge. Siemens’ control centre shows how AI can support this goal by optimising HVAC operation in real time. 

By working within existing systems and adapting continuously, the platform helps facilities maintain comfort standards while lowering energy costs, supporting more sustainable building operations at scale.