Identifying Excessive Energy Consumption in the District Heating Sector

Challenge

Even newer, energy-efficient buildings can consume significantly more heat than expected. Poor system balancing, technical faults and high return temperatures can lead to unnecessary energy consumption while also reducing the efficiency of the district heating network.

The challenge is that the underlying causes can be difficult to identify through conventional measurements and inspections. Better methods are therefore needed to compare buildings, detect deviations and identify where improvements can be made.

Solution

The project develops and tests a data-driven method for identifying energy waste in buildings connected to district heating. Data from HOFOR and Aalborg Forsyning is collected in a scalable data hub and analysed using AI and machine learning to benchmark energy consumption and return temperatures and detect abnormal patterns.

The digital analyses are combined with on-site testing of technical installations in selected buildings to identify the causes of deviations and validate specific improvements. The project will also provide the foundation for a scalable energy optimisation solution and, in the longer term, a digital energy label.