Innovation: From Energy Labeling Data to Usable Knowledge – With AI – EMAI Project

Customer: EMAI Project

In brief

4BC has initiated an innovation project that demonstrates the potential of applying AI to energy labeling data, making data more accessible to everyone in Denmark.

As the initiator of the EMAI project (Improving the utilization of data from energy labels with new AI analysis techniques), 4BC has bridged the gap between energy labeling and artificial intelligence. In collaboration with Alexandra Institute and CB Group, the project has developed a prototype of an AI chatbot that can retrieve and present energy labeling data in an accessible way – benefiting craftsmen, energy advisors, and homeowners.

Energy Labeling Data Must Be Accessible to Everyone

Energy labels contain valuable information about the condition of buildings – type and number of windows, insulation thickness, ventilation, pumps, etc. But the data is difficult to access and understand for the craftsmen and advisors who need to use it in practice. The consequence is that energy labeling data is currently rarely actively included in bidding, advice, and decisions about energy improvements.

Results

AI Chatbot Prototype for Energy Labeling Data

The project has developed a prototype that can answer specific questions about buildings' energy status without the need for manual data review.

Basis for Target Group-Specific Solutions

The project has documented a clear market potential for target group-specific AI solutions based on energy labeling data.

Bridge Between Energy Labeling and AI

The case demonstrates 4BC’s core competence in translating complex energy data into usable AI solutions with real value for practice.

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