# Triple Validation of Calibrated Building Energy Models with Different Air Infiltration Values
## 1 Introduction
### Context and Background Work
RL, which often leverages DL, enables iterative learning through reward feedback, improving over time but requiring a clear environmental model and careful reward design. [PERSON] and [PERSON] conclude that DL surpasses traditional algorithms in decision-making, driven by its advanced generalization abilities and increased computational resources. ML methods have also been directly applied to infiltration studies. [PERSON] et al. [31] introduced a hybrid approach combining data-driven ML with physics-based modeling to improve infiltration predictions. They use empirical air tightness measurements (e.g., from tracer gas tests) to estimate infiltration values and compare these values to the results of their hybrid machine learning and gray-box modeling methodology. This approach, using a random forest algorithm, enhanced traditional gray-box models by incorporating physical principles, achieving a 43% improvement in model accuracy and a 38% increase in generalization to new conditions. Additionally, [PERSON] et al. [32] proposed a simplified method that measures airflow through building envelopes by processing a single thermograph image with a pre-trained neural network, making it practical for field applications. Their neural network model achieved an accuracy of 98.10% for sequential samples and 93.30% for instantaneous samples. While in situ tests offer a promising approach to measuring air leakage, various methods have been employed to incorporate air infiltration into the calibration of building energy models. [PERSON] et al. [33] calibrated a historic BEM using the infiltration model (Equation (1)), integrated into EnergyPlus. Due to the low air tightness of the building, their blower door test could not reach 50 Pa. To compensate, they distributed the average airflow value from the blower door test across the number of windows in the room. Their sensitivity analysis revealed that variations in infiltration rates were the primary reason for the building's hourly indoor temperature changes and daily peak energy demands. Additionally, the analysis identified infiltration rates as one of the sources of the highest uncertainty in their calibration process. [PERSON] et al. [34] used a genetic algorithm (GA) to calibrate the air infiltration rate and the U-value in their building energy model. The GA aimed to find a near-optimal solution by selecting one of the seven predefined air infiltration values in a set. This selected value was subsequently applied in a regression process, where the objective function minimized the discrepancy between the model's predicted heat consumption and the actual measured heat consumption data for the same period. The results demonstrated that the method was effective in finding an air infiltration value that improved the model's prediction accuracy. However, it is important to note that they did not perform any field measurements to directly validate the calibrated infiltration rate with a measured value. [PERSON] et al. [35] also calibrated the infiltration value for their case study. They used the UNI 10339 Standard to calculate an airflow rate corresponding to a building with high permeability, specifically 0.15 ACH. Subsequently, they calibrated this parameter by setting a range of values around the calculated value. Their calibrated model achieved good agreement with the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) criteria [36]. On the other hand, there are other studies that employ building energy calibrations with infiltration values measured on-site. [PERSON] and [PERSON] [37] investigated the impact of air infiltration on natural gas consumption by conducting an in situ experiment in multi-unit residential buildings. Their focus was on ECMs targeting infiltration. Understanding the relationship between air leaks and gas use was crucial for identifying the most effective leak-fixing strategies for energy savings.