Cohort: 2 

Location: Rueil-Malmaison, France 

Application Title: On-device AI for efficient room control including HVAC energy optimization and automated comfort management 

Team Members: Philippe Rambach, Chief AI Officer; Benjamin Norman, Senior Principal, AI Product Manager; Scott Brown, AI Engineering Senior Manager; Mutaz Mujad, Product Manager (HVAC) Smart Room Control; Mark Van Den Broeke, Principal Project Design Leader

The challenge 

Buildings account for nearly 38% of global energy consumption, with heating, ventilation and air conditioning (HVAC) systems representing the largest share. HVAC systems regulate temperature, humidity and air quality, making them essential for occupant comfort, but they are also among the most energy-intensive components of the built environment. 

As cities expand, weather patterns become more extreme and expectations for indoor comfort increase, demand for HVAC continues to rise. Facility managers must balance comfort with energy efficiency, often across diverse building types and usage patterns. Traditional control systems rely on static schedules, fixed setpoints and manual adjustments. These systems respond slowly to changing conditions such as occupancy, weather or internal heat gains, leading to unnecessary energy use. 

Cloud-based optimisation tools can improve performance, but they depend on reliable connectivity and centralised data processing. In many buildings, particularly those with privacy, security or operational constraints, cloud reliance limits feasibility. 

The solution 

Room-level optimisation using on-device AI 

Schneider Electric developed an AI-enabled room controller that performs HVAC optimisation directly on the device, without relying on cloud connectivity. 

The controller runs a Dynamic HVAC Optimisation algorithm entirely at the edge. It continuously measures local conditions, including temperature, humidity and occupancy, and predicts how each space responds to heating or cooling. Based on these predictions, the system automatically adjusts setpoints and start–stop times to maintain comfort while reducing energy use. 

Each room or zone is treated as an independent system. Rather than applying a single building-wide model, the controller learns how individual spaces behave over time and adapts control strategies accordingly. 

How it works 

Local learning without pre-training 

The system uses unsupervised learning to build thermal models directly from time-series sensor data and local weather inputs. Because learning occurs on-device, there is no need for pre-trained models, historical data uploads or continuous connectivity. 

The AI runs on low-power microcontrollers and updates models within milliseconds, allowing real-time response to changing conditions. All computation remains local to the controller, giving building operators full control over data and ensuring privacy. 

The solution is designed as a plug-in upgrade and can be deployed across a wide range of HVAC equipment, including fan coil units and rooftop systems, without requiring additional hardware or software layers. 

How it differs from conventional approaches 

Decentralising control to the room level 

Conventional building management systems optimise HVAC performance at a central level, often using averaged data and static rules. Schneider Electric’s approach decentralises optimisation, allowing each room to be controlled based on its actual behaviour. 

By embedding intelligence directly into room controllers, the system avoids delays associated with central processing and reduces dependence on network availability. This makes it suitable for retrofitting existing buildings and for sites where connectivity or data governance is a concern. 

Impact 

• 5–15% reduction in energy consumption within the first two weeks of deployment 
• Indoor comfort maintained in line with ASHRAE 55, a widely used global standard for thermal comfort 
• Site-level emissions reductions observed in pilot buildings 
• Thousands of devices deployed across four pilot sites, including Schneider Electric offices and early adopter clients 
• Fully edge-based operation supports data privacy and simplifies integration with existing systems 

Why it matters 

Improving efficiency where energy is actually used 

HVAC efficiency is often discussed at the building or grid level, but control decisions ultimately occur room by room. Schneider Electric’s approach shows how on-device AI can support more responsive HVAC control without adding complexity or external dependencies. 

By reducing energy use while maintaining comfort, room-level optimisation offers a practical pathway to improving building efficiency as energy demand and climate pressures continue to grow.