## 3 Results
### Model Evaluation  
Based on the results obtained in the previous section, the XGBoost algorithm was chosen to be used, calibrating its parameters to develop the complete model. In Figures 4 and 5, we can see the graph comparing the simulated values using ML and the actual values from the EPCs, in both NRPEC and GHG emissions. The results show a capability to predict the aggregate energy performance of buildings with high precision. The results of the NRPEC model at the single-building scale show a normalized mean bias error (NMBE) of \(-0.027\%\) and a coefficient of variation of the root mean square error (CV(RMSE)) of \(37.7\%\). These metrics indicate that the model performs accurately in aggregate, as its NMBE of \(-0.027\%\)--closer to 0 than the ASHRAE standard of \(\pm 5\%\)--demonstrates minimal bias in predictions. However, its CV(RMSE) of \(37.7\%\) (ideally closer to 0) surpasses the ASHRAE benchmark of \(15\%\), suggesting less reliable precision in individual building estimates. When buildings are analyzed in aggregate, the model's performance improves markedly, meeting or exceeding ASHRAE standards. In fact, aggregating buildings by typology and city achieves compliance with ASHRAE criteria in \(99\%\) of cases for groups with over 100 buildings. It is important to note that the ASHRAE guidelines apply specifically to models in the 'UBEM for building-level recommendations' category, which require detailed knowledge and high precision for each individual building--expectations that exceed the objectives and capabilities of this model. The presented nUBEM falls under the 'UBEM for stock-level carbon reduction strategies' category, where precision requirements are less stringent. Despite this, the model performs commendably close to ASHRAE standards. The low NMBE value shows it is highly capable of predicting overall building stock performance, while the higher CV(RMSE) reflects expected limitations, particularly due to limited information on outliers. Notably, these results align well with findings from [PERSON] et al. [23], who documented similar trends in accuracy for aggregated building data in UBEM studies. Consistent with existing literature, the elevated CV(RMSE) at the individual-building level is a typical outcome for large-scale urban building energy models (UBEMs). Figures 4 and 5 further illustrate these results, showing that the model performs well in predicting aggregate building performance but encounters challenges with anomalous individual values. In a similar manner, the GHG emissions prediction shows similar trends, with an NMBE of 0.049% and a CV(RMSE) of 43.3%, leading to conclusions similar to those presented in NRPEC. It should be noted that the increase in data dispersion is due to the lack of information regarding the energy vector in the open data, which adds a layer of uncertainty when making predictions. The model's primary aim is to estimate NRPEC and GHG emissions for large groups of buildings, facilitating the calculation of progress indicators and the identification of poorly performing areas. While it does not provide precise predictions for every individual building in the Spanish residential stock--due to limitations in available data--it effectively assesses the performance of different city areas and analyzes the characteristics of buildings with low energy efficiency. In Table 2, a comparison is presented between the model proposed in this study, referred to as nUBEM, and other models in scientific literature. The table indicates the type of model according to its purpose, the number of buildings used to evaluate the model, the scale of the model, and its accuracy in terms of individual building values and aggregated building results. As can be seen in the table, the nUBEM results are very satisfactory, with  
Figure 5: Heatmap comparing GHG measured versus predicted data. Each pixel represents a range of 1 kgCO\({}_{2}\)eq/(m\({}^{2}\)y), and the color indicates the number of data points in each pixel.