# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 1 Introduction
### Context and Background Work
To achieve this, they carried out window component infiltration testing and incorporated the results into their building energy calibration method. This method isolated infiltration occurring solely through the windows (fenestration) and infiltration happening through both the window frame and the area where the window meets the wall (fenestration-to-wall interface). This experiment allowed them to compare two calibration methods: the traditional method with an infiltration value for the whole building calculated with DFR (Equation (1)) and off-the-shelf coefficients and a component-weighted method that integrated the results of the window component infiltration rate test. The calibration with component-weighted infiltration rate was considered the most suitable for the case study. Recent studies have focused on calibrating BEMs using air infiltration as a key tuning parameter. [PERSON] and [PERSON] [38] calibrated their model by adjusting each parameter within a defined range of variation (ROV). They only modified the infiltration rate in the third iteration, as earlier adjustments to the wall and ground-floor U-values did not meet ASHRAE's energy consumption criteria. Starting from an initial infiltration value of 1.1 ach (from design documents), an increase to 1.2 ach provided the best results, underscoring the parameter's sensitivity in simulations. [PERSON] et al. [39] calibrated four rooms by estimating air infiltration based on uncertainty, given limited data (the building was constructed in 1958). Their sensitivity analysis identified infiltration rate as the second most influential factor for indoor temperature. Using a multi-generational calibration model with Pareto optimization, they ran 1670 simulations for one parameter range and 1550 for another, selecting the first range based on MAD, RMSE, and NMBE criteria. Moreover, [PERSON] et al. [40] incorporated the ELA model (Equation (3)) into their building energy model calibration method. The air leakage area value was parameterized according to a set of values. The calibration process aimed to find the model that minimizes the difference between measured and simulated indoor dry-bulb temperatures. In another study [41], researchers employed the Hybrid model of EnergyPlus, which calculates dynamic air infiltration relying on measured temperature [42]. These infiltration values were then integrated into the calibration method and achieved good results compared to the aforementioned described method using the ELA model. While these previously mentioned studies aimed to provide a more accurate air infiltration value for building energy calibration, they lack validation against on-field measurements. Infiltration influences not only temperature but also the humidity levels in each zone, as air exchange impacts the moisture balance within rooms. Therefore, in the study of [PERSON] et al. [43], the calibration is multi-objective, aiming to identify the optimal values for infiltration airflow and moisture buffer masses that minimize errors in simulated temperature and relative humidity. First, infiltrations within the houses are modeled in TRNFLOW as cracks, representing airflow connections between an interior and exterior air node. The calibrated model achieved low RMSE values for both temperature and relative humidity, demonstrating its effectiveness in capturing realistic environmental conditions. Specifically, areas like the sleeping room, where closed doors reduce airflow, highlighted the importance of fine-tuning infiltration values to reflect distinct moisture behaviors accurately. These findings emphasize that carefully calibrated infiltration rates are essential for reliable indoor climate simulations, which are crucial for occupant comfort and building energy management. In another study, [PERSON] et al. [44] evaluated 60 calibration approaches using different optimization techniques for white-box BEMs. Each approach combined various error functions and datasets, enabling a comparison of accuracy and robustness in calibration results.