# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 1 Introduction  
### Context and Background Work  
It is widely known that air infiltration is a crucial element influencing both building energy efficiency and indoor environmental quality (IEQ). Up to a third of heating energy is wasted due to air leakage through the building envelope [1]. Therefore, accurately quantifying these losses is crucial for effective building energy rating [2, 3]. This uncontrolled air movement from the outdoors into a building not only impacts energy efficiency and thermal comfort but also significantly affects indoor air quality as it can serve as a pathway for pollutants and particulate matter [4]. Inaccurate quantification of air leakage can significantly impact building performance, leading to improperly sized mechanical systems and unreliable energy simulation results for both winter and summer conditions [5]. The influence of air leakage on energy consumption can overshadow the benefits of other building envelope improvements, such as enhanced insulation or glazing. Consequently, precise air leakage prediction and measurement are paramount for achieving high-performance building designs [6]. As the building industry increasingly relies on whole-building energy models to inform retrofit initiatives, the need for robust calibration strategies becomes imperative. Calibrating building energy models (BEMs) enhances the accuracy of energy consumption simulations by aligning virtual models with real-world building performance through the adjustment of model parameters based on in situ measurements [7]. This adjustment enables a more precise evaluation of retrofit project impacts. According to [PERSON] et al. [8], energy models are part of the third phase of a retrofit project and through their application, the performance of various retrofit alternatives can be quantitatively evaluated. However, measured data from in situ monitoring can be challenging to obtain unless the building has a building management system (BMS) that records sensor data [9]. Reliable data simplify BEM calibration, while data uncertainties require more assumptions, which heavily influence the process [10]. Furthermore, some parameters cannot be directly measured by sensors and require data from in situ experiments for their calculation. For instance, infiltration rate data often carry significant uncertainty in BEMs, especially when site-specific measurements are unavailable [11]. As highlighted by [PERSON] et al. [12], reliance on imprecise infiltration data introduces substantial uncertainty into both the model's quality and its energy performance assessments, limiting the insight BEMs can provide into potential energy savings from energy conservation measures (ECMs) aimed at reducing infiltration. [PERSON] et al. [13] evaluated the impact of retrofits on indoor air quality (temperature, humidity, dust levels) and energy consumption. They carried out tracer gas and bluer door tests to measure air infiltration before and after retrofits and used EnergyPlus to simulate energy consumption to compare with actual energy consumption data. Their findings showed that retrofits reduced indoor dust levels and improved building air tightness, demonstrating the effectiveness of targeted retrofits in reducing air infiltration, which directly impacted both indoor air quality and energy consumption. Accurately quantifying air infiltration can be cumbersome, with current models often unsuitable for design purposes due to standardization issues [14]. In a recent, [PERSON] et al. [15], discuss the history and theoretical foundations of infiltration and airflow models, highlighting their evolution over decades and integration into various building energy simulation (BES) tools.