# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 1 Introduction
### Context and Background Work
[PERSON] and [PERSON] developed correlations for small detached residential buildings [24]): \[I=(F_{sch})\frac{A_{L}}{1000}\sqrt{C_{s}\Delta T+C_{w}(WS)^{2}}\] (3) where \(F_{Sch}\) is a value from a user-defined schedule; \(A_{L}\) is the effective air leakage area in cm\({}^{2}\) that corresponds to a 4 Pa pressure differential; \(C_{s}\) is the coefficient for stack-induced infiltration in \((\)L\(/\)s\()^{2}/(\)cm\({}^{4}\)K\()\); \(\Delta T\) is the absolute difference in dry-bulb temperature between the average zone temperature and the average outdoor temperature in \({}^{\circ}\)C; \(C_{w}\) is the coefficient for wind-induced infiltration in \((\)L\(/\)s\()^{2}/(\)cm\({}^{4}\)(m\(/\)s\()^{2}\)\()\); \(WS\) is the local wind speed in m/s. These models require coefficient values that can be obtained through in situ experiments or provided by building simulation engines such as EnergyPlus, DOE-2, and BLAST. Identifying the exact coefficients for each building is a challenging task, causing modeling experts to frequently depend on fixed values from regulations or existing field tests [25] [PERSON] et al. [26] state that while a purely simulation-based or comparative simulation study may use a generic infiltration model available in existing building simulation programs, a validation study requires experimental methods, such as the tracer gas or blower door tests, to develop a new model. In their study, they conducted a 5-day tracer gas test to measure the air change rate (ACH) and a blower door test to determine the \(I_{design}\) in Equation (1). The coefficients were derived from the measured ACH and \(I_{design}\) values, reflecting the environmental effects of temperature differences between indoor and outdoor conditions as well as wind speed. They found that the existing default infiltration models did not align well with experimental data and required revision to achieve good agreement with the experiments. Another method, known as pulsed pressure, is used to assess air leakage. This technique involves injecting compressed air bursts into a sealed space and measuring the resulting pressure changes. These devices operate at pressure differentials (e.g., 3-10 Pa) closer to those experienced by buildings in use [27]. The main findings of [PERSON] and [PERSON]'s [28] research indicate that the pulse method is a reliable, minimally invasive alternative for airtightness testing in new builds and Passivhaus properties. Operating at ambient pressure, it preserves the building envelope and offers test repeatability with an average relative percentage difference of 4.96%. The pulse method also produced consistent results in Passivhaus properties and measured air permeability within a 10% error margin compared to fan pressurization. Additionally, while the power law model accurately predicted air leakage at high pressures, the conversion formula (developed by CIBSE TS23:2022 as an alternative of the power law equation [29]) showed even better correlation, suggesting its reliability for converting air permeability between high and low pressures. In addition to the decay equation and EnergyPlus infiltration models, various machine learning (ML) methods have been developed to enhance air infiltration calibration in building energy simulations. Traditional sensor-based measurements, such as tracer gas and blower door tests, provide high-accuracy, real-time data but can be costly and require extensive setup, particularly in complex buildings. To address these limitations, ML approaches, including deep learning (DL) and reinforcement learning (RL), offer alternatives with enhanced generalization and performance. [PERSON] and [PERSON] [30] discuss DL applications across building functions such as occupancy estimation, thermal leakage detection, thermal comfort design, and data generation. While DL algorithms excel in extracting features from raw sensor data, they demand large datasets, high computational power, and substantial memory.