Data Modelling for Digital Twins in the Building Sector

Abstract

Through the collection of data via sensors in smart buildings, value can be created by assessing the collected data, and thus derive the potential energy-saving measures, reduce water consumption, and more. Metadata exists about the sensors installed, but the Linked Metadata which would allow to easily understand the dependencies of the data collected, e.g., on the thermal level, is missing. This results in a lack of knowledge about the relationship of the data recorded, the physical location of the sensors, and what is measured in detail. Consequently, a lot of implicit expert knowledge is associated with the data.