A Reasoning Engine Architecture for Building Energy Metadata Management

During the Buildings’ lifecycle, massive amounts of data, that contain information related to their energy consumption, are generated. Towards the creation of smart building networks, this produced information must be intercepted and harmonized according to building ontologies and schemas. The pattern recognition from building metadata is based on inferencing and intelligent querying, that can be achieved with the utilization of graph and property databases that deploy and host building information. This paper presents a Reasoning Engine Architecture implemented in the context of the H2020 project called MATRYCS that persists building semantic information. It will be leveraged to support real life applications by improving the inference operations.