## 3 Computational Methodology
### Building Model Calibration  
Since building energy models are complex and comprise a large number of input data, it is necessary to carry out calibration processes to corroborate and verify that they represent the actual behavior and performance of the building. The literature reports two statistical approaches commonly used to assess the quality of model calibration: the mean bias error (MBE) and the coefficient of variation of the root mean square error (Cv(RMSE)) [28].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Loads**} & \multicolumn{5}{c}{**Period**} \\ \hline \multicolumn{2}{c}{} & \multicolumn{5}{c}{**Weekdays**} \\ \cline{2-7} \multirow{5}{*}{Occupancy} & Schedule & 00:00–7:59 h & 8:00–13:59 h & 14:00–16:59 h & 17:00–20:59 h & 21:00–23:59 h \\  & Percentage & 0\% & 100\% & 0\% & 100\% & 0\% \\ \cline{2-7} \multirow{5}{*}{Occupancy} & \multicolumn{5}{c}{**Weekend (Saturday)**} \\ \cline{2-7}  & Schedule & 00:00–7:59 h & 8:00–15:59 h & 16:00–23:59 h & 16:00–23:59 h \\  & Percentage & 0\% & 0\% & 100\% & 0\% \\ \hline \multicolumn{2}{c}{} & \multicolumn{5}{c}{**Weekdays**} \\ \cline{2-7} \multirow{5}{*}{Equipment and lighting} & Schedule & 00:00–7:59 h & 8:00–13:59 h & 14:00–16:59 h & 17:00–20:59 h & 21:00–23:59 h \\  & Percentage & 0\% & 100\% & 0\% & 100\% & 0\% \\ \cline{2-7} \multirow{5}{*}{Natural Ventilation} & \multicolumn{5}{c}{**Weekend (Saturday)**} \\ \cline{2-7}  & Schedule & 00:00–7:59 h & 8:00–15:59 h & 16:00–23:59 h & 16:00–23:59 h \\  & Percentage & 0\% & 100\% & 30\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage distribution of loads in the process of energy simulation.  
MBE measures the overall systematic error of the building energy model and is defined as follows:  
\[MBE=\frac{\sum_{i-1}^{Np}(m_{i}-s_{i})}{\sum_{i-1}^{Np}(m_{i})} \tag{1}\]  
where \(m_{i}\) and \(s_{i}\) represent the measured and simulated energy data, respectively; with \(Np\) as the number of measurements analyzed. The acceptable ranges of MBE for analysis with monthly data are between 0-5%, while for hourly or daily data the tolerance is up to 10% [29]. On the other hand, the CV(RMSE) is used as a complement to prevent calibration errors due to the compensation between positive and negative values used in the MBE. The CV(RMSE) is computed following the equation:  
\[CV(RMSE)=\frac{\sqrt{\sum_{i-1}^{Np}(m_{i}-s_{i})^{2}/Np}}{\overline{\sum_{i-1} ^{Np}m_{i}/Np}} \tag{2}\]  
According to ASHRAE and the International Performance Measurement and Verification Protocol (IPMVP), the acceptable range for CV(RMSE) using monthly data is from 0 to 15%, while hourly and daily data this extends up to 30% [29].  
Figure 5 illustrates the results of the outpatient medical center model calibration concerning the cumulative monthly energy consumption over a year. In general, the overall systematic error of building model (MBE) is close to 1%, and the CV(RMSE) is 7.75%. This implies that the model balances well between overestimation and underestimation, and the CV(RMSE) confirms acceptable uncertainty range below the limit, indicating a good accuracy according to the regulations. It is essential to highlight that the largest monthly difference occurs in May, without exceeding 10%, and that, on average, the monthly differences of the model do not exceed 6%. Furthermore, the differences presented in the warmer months overestimate the monthly demand when analyzing the integration of passive technologies.