InGrid – Smart heat networks

With InGrid we enable rapid assessments of how assets interrelate geovisually, enabling you to view and easily understand events in real-time or as required. Going beyond typical data setups geared towards reactive incident response and invoicing, deep real-time insights into your heat grids enables lower operational temperatures and capacity. This operational efficiency frees up the capacity to extend the network – which InGrid will help you plan by automatically plotting and suggesting network extensions through machine learning.


Geovisualizing heat networks

Resolve challenges quickly by visualizing assets and identifying sources of issues. Through easy, user-friendly access to asset locations and how they are interrelated, insights are at your fingertips. Beyond such ad hoc views of your network and assets, this platform allows real-time predictive alerting on system conditions that are not typically found SCADA asset control systems.




Plot new heat grids or extensions

By automatically plotting the most cost-effective (extensions of) heat networks with our machine learning algorithms, we save time and money in the design process of heat networks. This tool is also of use in identifying current bottlenecks in relation to demand, and automatically proposing solutions to the problem. Furthermore, by including statistical data on heat consumers in an area, in combination with building age and thermal properties, it is possible to accurately estimate future demand and scope for expansion.

Demand side management

Heat demand is typically not flexible. By taking into account the start-up speed, cost-structure, and storage properties of demand locations, we are able to mix and match optimally towards the lowest operational costs. In addition, this dynamic control system enables lower system temperatures to reduce dependence on fossil fuels and increase base load on low-carbon heat sources and storage systems. This enables low-carbon, cost-efficient comfort.