## 3 Results
### Model Evaluation
(2: UBEM for stock-level carbon reduction strategies, 3: UBEM for building-level recommendations, UBEM for buildings-to-grid (B2G) integration, NMBE: normalized mean bias error, CV(RMSE): coefficient of variation of root mean square error, R\({}^{2}\): coefficient of determination, NRPEC: non-renewable primary energy consumption, GHG: greenhouse gas emissions, EC: energy consumption, AEU: annual energy use, HD: heating demand, AED: annual energy demand). data, such as use, construction date, or number of dwellings, while demanding fewer computational resources to manage. Regarding the regional-scale model presented by [PERSON] et al., the model classifies the housing in the region of Sicily into archetypes, obtaining aggregate results that underestimate the total energy consumption by 7.77%, which is considered within acceptable limits. However, as the results can only be compared at a regional scale, it is not possible to determine whether the generated archetypes accurately represent the housing or if overestimations and underestimations are compensated for at the regional level. At this point, the use of EPCs to generate the model allows for a more detailed analysis of the results, enabling the assessment of whether the simulations accurately reflect the energy behavior of buildings. The nUBEM model achieves a total deviation much lower than that of [PERSON] et al. (where closer to 0 is better). However, it is important to note that as more data are incorporated, the results are likely to improve. Additionally, the model should be compared at an individual scale to facilitate more accurate assessments of its performance. Compared to city-scale models, this model still achieves very good results, both at the individual and aggregate levels, as it has been able to learn from a large number of patterns due to the availability of a significant amount of data. District-scale models, however, show better performance than our model, mainly because they are able to identify and handle infrequent cases more effectively, as they typically have a much more detailed and higher-level information, resulting in higher CV(RMSE) and R\({}^{2}\) values. For instance, [PERSON] et al. present two district heating systems with satisfactory results in terms of precision, particularly in the R\({}^{2}\) metric, especially when incorporating additional results. By examining their figures, the model reflects reasonable accuracy across various building types. However, as the authors highlight, the model fails to provide valid results at the individual archetype scale due to high CV(RMSE) values and a limited number of samples. Nonetheless, the aggregated results are already acceptable for small numbers of buildings. [PERSON] and [PERSON] employ a hybrid approach for modelling heating and cooling energy consumption of building stock at the district scale, utilizing diverse data sources on buildings and supplementing them with national statistical data. This results in highly detailed archetypes, incorporating factors such as air infiltration, window-to-wall ratio, and occupant, equipment, and lighting density, among others. These detailed models are used to train various machine learning models, achieving a high level of result precision.