The building sector is one of the biggest energy consumers in Germany, alongside industry and the transport sector. The building sector alone accounts for 30% of the total energy consumption with a consumption of approx. 2400 petajoules. If we are to achieve the climate protection targets we have set ourselves, we also need to find ways of saving energy in this sector.
However, experience has shown that good planning does not immediately result in an energy-efficient building. Rather, it is the consumers themselves that are decisive in determining the actual energy consumption. Big data and digitisation open up new ways of forecasting consumption and controlling it during operation. This is where a research project of a German consortium comes in. Within the DataFee project, methods and models are being developed to forecast the actual energy consumption particularly with regard to user behaviour.
First, the classifications for the different types of user behaviour must be identified and described. The classification can, for example, depend on the working hours or the desired room temperature. In addition, self-learning algorithms are being developed that enable predictions and the targeted control of user behaviour. An information system will be used to provide users with feedback on the possibilities of how energy can be saved and the room temperature adjusted.
In order to simulate the whole system, a digital ‘twin’ of this building automation system will also be created on the basis of which the various control strategies can be tested and improved.
The research project is being funded by the Federal Ministry for Economic Affairs and Energy from 2019 to 2022.