Information Integration and Semantic Interpretation for Building Energy System Operation and Maintenance

Digitalization in facility management has become an active field of research for many reasons. Among others, it can provide high support by saving energy and cost in the building sector as well as for optimizing internal processes involved within the management of a facility. To enable this, many data about a building has to be collected and analyzed. Building management systems can already produce huge amounts of data that are analyzed for supervising, controlling and benchmarking buildings. However, even if numerous data are produced during building operation there is no much use of building information created during its design. In view of that, this research proposes a methodology and a software framework for closing the informational gaps between building design and operation. It follows data integration steps that use initial building information models and extend them with operation data as well as semantic models. These data models are used for interpreting energy system behaviors and performing predictive analyses. More specifically, a system ontology is introduced that supports reuse of knowledge for optimized building operation and maintenance.