The energy consumption and CO² emissions of a building are determined on the one hand by the building's structural specifications and on the other hand by the actions of the building's users. Some structural aspects can be optimized with small and/or quickly recouped investments. However, thorough renovations are expensive and have long payback periods.  

Therefore, with this project the Flemish Energy Agency wants to investigate how much energy can be saved by automatically adjusting the various utilities (lighting, heating, ventilation, cooling) of the building to the actual needs of the users. Thus, they want to eliminate consumption to the maximum when there are no users. When there are users, they want to optimize consumption while maintaining comfort and user experience.  

By capturing data regarding the use of the building, they want to create a self-learning system that can predict when there will be consumption and thus further optimize the control. To do this as accurately as possible, they want to provide control and data capture per room. This will give them maximum control over, and insight into, each consumption location. This will allow them to allocate consumption and costs much more accurately and work on positive behavioral change based on that.

Furthermore, with this project they want to investigate whether the fully digital interaction promotes user adoption and whether additional gains can be realized in other areas such as capacity and occupancy, security, personnel costs and optimal choices when investing in renovations.