## 1 Introduction
### Literature Review  
In large-scale analysis, UBEMs are useful tools to evaluate the energy performance of the buildings and potential improvements. UBEMs are classified depending on their purpose and objectives into four categories [19]:  
* UBEMs for urban planning and new neighbourhood design
* UBEMs for stock-level carbon reduction strategies
* UBEMs for building-level recommendations
* UBEMs for building-to-grid (B2G) integration  
Since the objective of our nUBEM is obtaining progress indicators for the evaluation of the evolution of building decarbonization and of the impact of energy renovation policies at a national scale, our model falls into the category of UBEM for stock-level carbon reduction strategies. Regarding the methodology employed in the creation of UBEMs, these can be classified into two main types [20]: top-down models and bottom-up models. Top-down models are primarily based on the interrelations between the energy sector and the broader economy, and they do not require detailed building information, whereas bottom-up models represent energy consumption, leveraging detailed information about energy end uses through aggregation. Specifically, bottom-up models can be developed using either statistical data about the energy use of individual buildings (statistical approach) or physical models that estimate energy consumption by modeling buildings' energy characteristics [21]. To determine a building's energy performance, the model can use either simplified methods, such as engineering approximations, or detailed approaches that characterize the building's energy behavior with greater precision [21]. Simplified engineering models offer lower accuracy compared to detailed models. Detailed models began utilizing individual buildings for smaller-scale analyses (individual approach) and archetypes for larger-scale studies (representative building approach). However, despite the use of archetypes, a significant challenge for detailed physics-based bottom-up UBEMs lied in optimizing their development process to reduce computational demands and time requirements [22]. In a previous study [9], we addressed the issue of the high computational demands of physics-based UBEMs by leveraging the energy performance certificates (EPCs) of buildings, proposing a new way of building detailed physics-based UBEMs. However, this UBEM was limited to energy data from the relatively small proportion of buildings that possessed EPCs. In this paper, we significantly enhance our previous model by integrating machine learning techniques into its framework. Figure 1 summarizes the possible methodological strategies for generating an UBEM as suggested in our previous study [9], with the addition of a hybrid approach. A hybrid model, according to [PERSON] [10], [PERSON] [23], and [PERSON] [24], combines energy models with machine learning techniques to generate models that not only include information about buildings with existing data but also predict the behavior of buildings without data. This hybrid methodology offers significant potential for developing UBEMs for stock-level carbon reduction strategies, as it allows us to expand the knowledge from the existing data collected to the rest of the buildings for which we have no energy performance information. As can be seen in Figure 1, there are three main ways to generate detailed physical bottom-up UBEMs: individual buildings, archetypes, and EPCs. For large scales, only the  
Figure 1: Scheme of different UBEM methods. The approach selected in this document (in red) and the new methodology proposed for national-scale UBEMs enhanced with AI (nUBEM) are marked. archetype and EPC approaches are suitable, as it is not feasible to model each building individually. Building archetypes are representations of a group of individual buildings through the use of a typical building, which are used to simplify the modeling of large areas [9]. Archetype-based UBEMs are widely used for stock-level carbon reduction strategies at different scales. For example, [PERSON] et al.