District heating networks often face inefficiencies, high energy losses, and costly peak loads. Yuon Optimizer uses AI and Model Predictive Control to balance production, distribution, and consumption automatically. It serves utilities and energy providers aiming to lower costs, emissions, and operational complexity.
Anticipates thermal demand based on weather forecasts, building dynamics, and consumption patterns to optimize production and distribution in real time.
Creates a digital twin model of the entire network to continuously simulate and optimize system performance, reducing peak loads and improving energy efficiency.
Automatically manages and optimizes thermal networks using advanced machine learning and predictive control. The software integrates seamlessly into existing infrastructure, enabling automated operation, adaptive control, and continuous performance improvement.
Yuon Optimizer is a software-only solution that can be quickly implemented without hardware changes. Its modular design allows utilities to scale from single networks to large multi-site operations efficiently.
Joint project by Yuon, Energie 360° and Hoval demonstrating 33% peak load reduction, 25% capacity savings, and energy savings in Zurich’s urban heating network through AI-driven predictive control.
Yuon Optimizer enabled real-time load management in an urban heating network. The pilot cut peak loads by 33% and reduced connected capacity by 25%, all with seamless integration and no comfort loss, demonstrating scalable impact for city utilities.
At the Länggasse district heating site in Bern, Yuon Optimizer reduced the 07:00 peak load by 60% (avg. 27%) and achieved 10% total energy savings without comfort loss, enhancing grid stability and efficiency for the municipal utility.
Yuon created a digital twin of the Münsingen North heating network to uncover hidden efficiency potential. The data-driven analysis revealed optimization levers for production, storage, and control. Forming the blueprint for the network’s smart expansion.