Embed machine learning directly in HVAC equipment controllers, enabling equipment to learn, adapt and optimize performance under real operating conditions.
Conventional HVAC controls rely on fixed settings that cannot adapt to changing operating conditions. For AHUs, this can mean defrosting too early or too often, wasting heating energy throughout winter.
Machine learning embedded in the Climatix controller learns the AHU’s normal, frost-free behavior. It detects signs of frost using existing operating signals and triggers defrost only when needed.
Help HVAC OEMs deliver more adaptive and efficient equipment while reducing unnecessary energy use, added hardware and setup complexity.
Intelligent defrost supports 20–30% lower heating energy use without extra sensors. No additional commissioning or set-up effort.*
*Results vary significantly by location, freezing hours, operating schedule, airflow, AHU size, heat-recovery efficiency, climate, and other assumptions.
From static defrost rules to self-optimizing AHUs, with no additional sensors. Siemens embeds machine learning directly into the Climatix controller platform.
The controller learns the AHU's frost-free fan power pattern as a virtual reference, no dedicated frost sensor required.
It continuously compares predicted, frost-free fan power with actual measured fan power in real time.
A growing deviation signals frost formation, validated in simulation, lab and field.
Defrost is triggered only when needed. The model keeps learning and refining with every cycle.
Equipment that continuously adapts to real operating conditions, triggering defrost only on demand through self-adaptive optimization directly on the controller, reliably preventing ice blockage while minimizing energy costs.
Learn normal equipment behavior from local operating signals and adapt within engineer-defined limits. Engineers define objectives, constraints, fallback behavior, commissioning intent and release validation.
Continuously refine the embedded model as operating conditions change, without cloud connectivity or large historical datasets.
An estimated 20–30% reduction for defined AHU scenarios without frost sensors, compared with preventive defrost.*
* Results depend on climate, freezing hours, operating schedule, airflow, AHU size, heat-recovery efficiency and other operating conditions. Results vary and are not guaranteed.
Climatix provides the eco-system to develop learning based functions at scale:
Whenever outside air temperatures drop below freezing, frost formation on AHU heat exchangers is unavoidable. Intelligent defrost learns an AHU’s normal, frost-free behavior and detects frost without additional sensors. The solution has been validated using AHU data, simulation, field testing and testing at the Siemens performance lab in Zug, Switzerland.