## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{\begin{tabular}{c} **E\({}_{\text{E}}^{\text{2018}}\)** \\ **(kWh)** \\ \end{tabular} } & \multicolumn{2}{c}{**Extra Consumption in Building B1**} & \multirow{2}{*}{\begin{tabular}{c} **E\({}_{\text{EB}}^{\text{2018}}\)** \\ **(kWh)** \\ \end{tabular} } & \multicolumn{2}{c}{**Extra Consumption in Building B1**} & \multirow{2}{*}{\
\begin{tabular}{c} **E\({}_{\text{EB}}^{\text{2018}}\)** \\ **(kWh)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **E\({}_{\text{EB}}^{\text{2018}}\)** \\ **(kWh)** \\ \end{tabular} } \\  & & & & & & \\ \hline January & 36,558 & 458 & 11,524 & 3218 & 21,358 & 3.10 \\ February & 34,923 & 503 & 12,100 & 3575 & 18,745 & 2.72 \\ March & 38,461 & 503 & 10,566 & 3575 & 23,817 & 3.46 \\ April & 27,612 & 230 & 6319 & 2503 & 18,560 & 2.69 \\ May & - & - & - & - & - & - \\ June & - & - & - & - & - & - \\ July & - & - & - & - & - & - \\ August & - & - & - & - & - & - \\ September & - & - & - & - & - & - \\ October & 34,640 & 334 & 1224 & 3933 & 29,149 & 4.23 \\ November & 39,116 & 503 & 8609 & 3575 & 26,429 & 3.84 \\ December & 32,893 & 321 & 6069 & 2145 & 24,358 & 3.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Determination of the electricity consumption per area in Building B1. To determine the heating demand in Building B2 for 2022 (\(D_{H}^{2022}\)), a hybrid methodology combining monitored data and calibrated modeling was employed. The natural gas consumption for the campus in 2022 (\(E_{NG}^{2022}\)), corresponding exclusively to Building B1, was used as the basis. The energy demand for heating in B1 was derived by applying the measured efficiency of the Low-Temperature Condensing Boiler (\(\eta_{LTCB}\)) obtained through in situ testing with a Testo brand flue gas analyzer (Model 310). To enhance the reliability of the calculation, a calibrated energy model for Building B1 was developed and validated against the monitored gas consumption data. This model incorporates thermal properties, occupancy schedules, and local climate data to simulate building performance under the observed conditions. The specific heating demand for B1 in 2022 was then calculated based on the heated surface area and, subsequently, the same method was implemented in Building B2. The simplified calculation process, which is presented in Figure 6, is juxtaposed with the results from the calibrated model to validate the assumptions and demonstrate the applicability of this alternative approach for similar studies. This dual-method strategy ensures that the derived heating demand values are both robust and adaptable for broader use in building energy performance analyses. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Month**} & \multicolumn{2}{c}{**Consumption**} & \multicolumn{2}{c}{**Consumption in B1**} & \multicolumn{3}{c}{**Consumption in B2**} \\  & **in B1 and B2** & & & & & **Consumption in B2** \\  & \(\text{E}_{\text{E}}^{2022}\) & \(\text{E}_{\text{E}-\text{P}}^{2022}\) & \(\text{E}_{\text{E}-\text{H}\text{P}}^{2022}\) & \(\text{E}_{\text{E}-\text{L}}^{2022}\) & \(\text{E}_{\text{EB}}^{2018}\) & \(\text{E}_{\text{EB}}^{2018}\) & \(\text{E}_{\text{E}-\text{W}\text{W}}^{2022}\) \\  & **(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** & **(kWh)** \\ \hline January & 129,816 & 458 & 11,011 & 3218 & 20,391 & 25,293 & 69,445 \\ February & 106,828 & 503 & 8339 & 3575 & 17,636 & 22,201 & 54,574 \\ March & 109,959 & 503 & 9433 & 3575 & 12,194 & 28,206 & 56,048 \\ April & 68,648 & 230 & 5377 & 2503 & 17,911 & 21,980 & 20,647 \\ May & - & - & - & - & - & - & - \\ June & - & - & - & - & - & - & - \\ July & - & - & - & - & - & - & - \\ August & - & - & - & - & - & - & - \\ September & - & - & - & - & - & - & - \\ October & 72,771 & 334 & 1921 & 3933 & 28,038 & 34,521 & 4024 \\ November & 78,298 & 503 & 2873 & 3575 & 25,352 & 31,298 & 14,697 \\ December & 80,157 & 321 & 4094 & 2145 & 23,698 & 28,842 & 21,057 \\ \hline \hline \end{tabular}
\end{table}