Ento Labs Aps & Høje-Taastrup Municipality

Globally, electricity consumption from buildings is the biggest source of carbon emissions. To meet the targets of emission reduction in 2030 and 2050, the biggest contribution must come from energy efficiency, but to date, there isn’t a good, scalable solution for improving energy efficiency in buildings.

The solution offers the development of a software solution, a platform, and a new business model using machine learning. The machine learning models will include more data than previous models, making the building comparisons more precise and resulting in suggestions for energy optimization.

A machine learning system that will understand the energy consumption in buildings based on information about the building, such as area size, number of stories, year of construction, source of heating, application, and so on.

Energy efficiency is considered to be one of the most important factors in the green transition, but it can be costly and difficult to install meters, gather and analyze data, prioritize and not least follow up on optimization measures. By using a data-driven, scalable system, the process will be considerably improved.


Ento Labs
Henrik Brink
E: henrik@ento.ai
T: +45 26 14 56 76

Høje-Taastrup Municipality
Jens-Emil Nielsen
E: jens-emilni@htk.dk
T: +45 43 59 12 73