Digitalisation of analytical functions for district heating utilities.

Important results from the project

The purpose of this project has been to develop a prototype system that uses advanced analytics, machine learning and cloud infrastructure to automate a set of analyses related to district heating. A pilot project was started with Göteborg Energi, Jönköping Energi and Fortum Oslo Varme as participants. A first set of analyses have been built into the system platform and tested with metering data from the participants networks. Outcomes have been very successful. The project has helped verifying the technology, methodology, customer needs and market acceptance for these services.

Expected long term effects

Utilifeeds long term ambition is that the platform should contribute to district heating utilities having better capabilities to meet future requirements that comes with a more flexible energy system, such as more efficient data flows and new business models. Such developments have the potential to improve the environmental impact of the whole energy system through better resource utilization. The system platform developed within this project is an important step for Utilifeed to move in the direction of this long term ambition.

Approach and implementation

Before the start of this project, Utilifeed had developed methodologies for analysis, machine learning algorithms for district heating and some micro services necessary to automate analysis in a cloud environment. Within this Vinnova project these early results have been further developed to a first working prototype where several microservices communicate to automatically in a standardized manner perform a number of analyses tasks on the big volumes of data generated by district heating networks.