Gradyent Digital Twin

by Gradyent

AI Application

Low Confidence

Gradyent's Digital Twin is a cloud model of a district heating or cooling network that combines geographical, weather and sensor data with physics-based models and AI to calculate temperatures, flows and pressures in real time. Operators use it to lower supply temperatures, cut heat losses, dispatch multiple heat sources and plan investments. At Eneco, the largest Dutch district heating operator, a twin of a network of about 10,000 homes and 32 substations uses smart-meter data, sensor data and weather forecasts to give dynamic setpoint recommendations to control-room operators; Gradyent reports supply temperatures 5-10 °C lower and more than 600 t CO2 saved per year. Ennatuurlijk built a twin of its Hengelo network (2022) to tune heat pumps and place a buffer, and Groningen's public heat company WarmteStad announced in February 2025 that it will use one for real-time dispatch and forward temperatures across its multi-source network for over 6,000 customers. Shell is piloting a twin of its Rotterdam steam grid (2025). In the Eneco case the twin recommends and control-room staff decide; the other sources do not describe the human role.