Intelligent load management for a sustainable, innovative and future-proof grid
Our software creates a digital twin of the thermal network and optimizes operations through improved load management: peak loads are smoothed out, temperatures are reduced and costs are lowered as a result. Different energy sources can be linked and dynamic energy prices can be integrated.
It automatically learns the thermal properties and energy requirements across the network and ensures efficient operation.
Intelligent load management
Load management in intelligent heating networks actively controls energy demand and distribution by partially shifting consumption. Knowledge of the network characteristics and the exact heat demand of the consumers is crucial for optimization.
Our software creates digital twins in real time and enables active monitoring and control of the thermal properties of the distribution grids and the connected buildings.
This allows load peaks to be smoothed out, heat sources to be optimally utilized, inactive buildings to be used as energy storage and local water reservoirs in the network to be actively managed.
Before
After
These key factors drive optimization success
Intelligent load management is made possible by taking a holistic view of the grid and is made up of a combination of individual measures or connection points, whereby the implementation of even one measure enables more efficient and ecological management.
Modern technology enables the realisation
Model Predictive Control
(MPC)
We predict the heat requirements in real time and control the grid to satisfy user heat requirements while minimizing losses. Our prediction algorithms make use of historical on-site data, on-site weather forecasts and internal know-how of other similar grids.
Digital twin
We create a digital twin of each building and the entire network, including real-time thermodynamics of each connected component. Precise real-time monitoring allows us to quickly respond to changes in requirements. It also allows us to automatically detect long-term changes in thermal properties of components, report potential leaks and sub-optimal components.
Artificial intelligence
(AI)
Our forecasting and control algorithms make use of state-of-the art machine learning and AI technologies to detect complex patterns in user day-to-day heat requirements, improving efficiency of our predictive heating strategies and saving energy.