Generating building performance simulation models using semantic graphs and sensor measurements

Building Performance Simulation (BPS) models are often used as test benches to optimize the operation of Heating, Ventilation, and Air-Conditioning (HVAC) systems in buildings. Since modelling a building and its HVAC systems demands intensive manual efforts, existing studies have explored generating BPS models semi-automatically using Industry Foundation Classes (IFC) files. However, such an approach focuses primarily on modelling the building envelope with a simplified system during the design stage because the IFC schema still lacks the vocabulary to describe the technical equipment, their control sequences, and sensor data comprehensively. This paper investigates how data transfer relying on Semantic Web technologies can reduce the modelling efforts in BPS. The metadata required in simulation, which includes building topology, HVAC systems, related sensors, and actuators, is described in the Resource Description Framework (RDF). The paper proposes a semantic graph-based workflow to generate a model consisting of building envelope and HVAC systems and then connect the model with the corresponding historical measurements. The workflow successfully generates a twelve-zone office building model with a simplified HVAC system. The CO₂ and temperature simulation results of all zones are compared with real measurements from 2022 -2023.