Extending Brick for automated comfort diagnosis
Modern buildings are data-rich environments that can contain thousands of IoT devices. However, most of this data is not analyzed in order to reduce energy consumption and improve occupants' comforts. This is often due to the required large manual effort for integrating the data into analytic systems. Semantic models allow to model the required meta-data and to arrive at an automated integration process. This is demonstrated for the new Brick ontology, that comprehensively models meta-data in buildings. It is extended by model concepts enabling to address challenges pertaining to physics and thermal comfort. Moreover, this Brick ontology is further extended by reasoning approaches in order to better exploit knowledge. As an example, the proposed approach is used to compute and diagnose virtual sensors so as to assess thermal comfort in a real building.