Leveraging zone air temperature data to improve physics-based energy simulation of existing buildings

The paper introduces a hybrid modelling approach that enhances the accuracy and usability of physics-based energy simulation for existing buildings. The approach leverages measured zone air temperature data streams—increasingly available from smart thermostats—to derive difficult-to-obtain input parameters for internal thermal mass and infiltration airflow rates. It does so using a reformulated inverse heat balance algorithm. We implemented the inverse algorithms in EnergyPlus and used LBNL's Facility for Low Energy eXperiments (FLEXLAB) for demonstration and validation