Bridging performance gap for existing buildings: The role of calibration and the cascading effect

Calibrating the building energy simulation (BES) models is a typical way to bridge energy performance gaps for existing buildings. Currently, evidence-based and data-driven calibration are both widely used and have their advantages and limitations. However, a systematic approach to combining the advantages of these two approaches has not been established. This study performed evidence-based and data-driven calibration consecutively using a real-world data-rich building, assuming different scenarios of data availability. 24 intermediate and ultimate models were obtained and comprehensively evaluated, by inspecting the calibrated parameters, calculating the predictive errors, and evaluating the energy conservation measures. It is shown that a satisfactory CV(RMSE) could be achieved even without any detailed evidence about the building, which was misleading due to the drifted parameter values. Accordingly, data acquisition recommendations were made considering the importance and acquisition costs. Moreover, despite similarly low errors, different models can estimate monthly energy savings and percentages with discrepancies exceeding 1000 kWh and 10%. Using a “calibrated” model without knowing the potential risk, the cascading performance gaps can lead to wrong decisions.