## 3 Results
### Model Evaluation
the model accurately predicting the building stock and achieving very positive values compared to those found in the scientific literature, especially in comparison with studies analyzing large scales, such as the presented study. At larger scales, the model achieves an R\({}^{2}\) value that is more precise than the one obtained with the AutoBEM model (where values closer to 1 indicate better performance). Moreover, by examining the graphs included in their paper [10], it becomes clear that the nUBEM model handles both deviation and data dispersion more effectively, resulting in a model that better represents energy performance. As highlighted in the same paper [10], the challenge of predicting the type of energy carrier used for heating adds complexity, which increases the margin of error. This is also reflected in the nUBEM model, where the results for predicting GHG emissions are slightly lower than those of the NRPEC model, partly due to the lack of information about the energy vector. A significant difference lies in the source of the data used. For similar data, such as physical and geographical characteristics of buildings, the information was obtained from very different sources. AutoBEM employs data derived from open-access sources that are not collected by official organizations (e.g., cadastral records or EPCs) but instead extracted from digital terrain models (DTMs), surveys, and annual maps of global artificial impervious areas. This approach has a great potential for direct application in other countries. However, it also increases the risk of mislabeled data and raises the computational requirements for processing the information. In contrast, leveraging public information sources such as cadastral records, responsible for collecting, processing, and publishing data, facilitates easier access to information. These public sources enable the collection of reliable building \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Characteristics** & & & \multicolumn{3}{c}{**Accuracy in Single**} & \multicolumn{3}{c}{**Accuracy in Aggregated**} \\  & & & & \multicolumn{3}{c}{**Building**} & \multicolumn{3}{c}{**Buildings**} \\ \hline \multirow{2}{*}{**Model**} & **Type of Model** & **Number of Buildings** & **Scale** & **NMBE \%** & **CV** **(RMSD \%)** & **R\({}^{2}\)** & **Deviation in Total Values \%** & **R\({}^{2}\) Total** \\ \hline nUBEM (this paper’s model) & 2 & 1,246,864 & Country & \(-0.027\) & 37.7 & 0.39 & \(-0.177\) & \multirow{2}{*}{0.998} \\  & & & & & & & (NRPEC) & \\ \hline nUBEM (this paper’s model) & 2 & 1,246,864 & Country & \(-0.049\) & 43.3 & 0.36 & \(0.198\) (GHG emissions) & \multirow{2}{*}{0.998} \\ \hline AutoBEM [10] & 2 & 50,843 & Country & - & - & 0.069 & - & - \\ \hline [PERSON] et al. [53] & 2 & 1,717,000 & Region & - & - & -7.77 (EC) & - \\ \hline [PERSON] et al. [54] & 2 & 899,740 & City & - & - & - & 5 (AEU) & 0.74 \\ \hline Gassar et al. [55] & 2 & - & City & - & - & - & - & 0.998 \\ \hline CESAR [56] & 3 & 78 & City & - & - & - & \(-1.05\) (HD) & - \\ \hline Johari et al. [57] & 3 & 2044 & City & 10 & 54 & - & 10 (AED) & - \\ \hline Johari et al. [58] & 4 & 2326 & City & - & - & - & 14 (AED) & - \\ \hline Aguacil et al. [35] & 3 & \begin{tabular}{c} 400 \\ (dwelling) \\ \end{tabular} & City & 25 & - & - & - & - & - \\ \hline Li and Yao [24] & 3 & 573 & District & - & 17.3 & - & - & - \\ \hline Heinderthaler et al. [27] & 3 & 53 & \
\begin{tabular}{c} District \\ heating 1 \\ \end{tabular} & 10 & 32 & 0.83 & - & - \\ \hline [PERSON] et al. [27] & 3 & 13 &
\begin{tabular}{c} District \\ heating 2 \\ \end{tabular} & \(-9\) & 44 & 0.76 & - & - \\ \hline [PERSON] et al. [44] & 2 & 245,490 & City & - & - & 4.5 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of UBEM models by purpose according to [19], number of buildings, scale and accuracy obtained for single building and aggregate total values.