Real-time building performance monitoring using semantic digital twins

Higher building performance saves operational costs, helps sustainability goals, and increases the comfort, satisfaction, productivity, and health of occupants. However, models simulating the occupants' perceived performance of indoor environments require highly heterogeneous input data. This paper aims to show how semantic web technologies can help to overcome problems deriving from this heterogeneity issue. Literature is studied to categorize the necessary input information for modeling occupants' perceived building performance. Two perspectives were consulted, namely the Indoor Environmental Quality approach and the Healing Environments approach. Based on the results, a core IEQ data model, integrating building topology, static properties, and dynamic properties, is proposed. We introduced a method to integrate this information by using the Building Performance Ontology (BOP). This method was tested by manually transforming the Open Smart Home (OSH) data to BOP vocabulary. Time-series data from various sensors were stored in InfluxDB and connected to the linked building data. A custom-built python tool was created to compute the thermal comfort of the OSH using the PMV/PPD method. The results indicate that a common data structure for static and dynamic properties helps to integrate the heterogeneity input data. Furthermore, we showed that adding occupants' data or system information could improve thermal comfort through multiple reactive approaches.