# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 4 Discussion  
The results obtained confirm the initial hypothesis that jointly analyzing large-scale building data and applying predictive ML models make it possible to accurately identify the parameters that most influence energy consumption. In particular, the performance of the building envelope (i.e., the thermal transmittance values of walls and glazed surfaces) was found to be the main predictive factor, which is consistent with previous reports in the literature [14,22]. Compared to other state-of-the-art approaches, the proposed model stands out due to:  
* High generalizability, thanks to the use of a large, representative dataset of real buildings. * Cross-validation with clustering, which has made it possible to define energy digital representative buildings and support targeted, non-uniform retrofit strategies. * Economic integration, with a quantitative assessment of ROI and payback times for each intervention measure. Many recent studies focus on using ML techniques to predict energy consumption or optimizing the physical simulation of retrofit interventions [16, 17, 18, 19]. However, these approaches typically treat energy, economic, and strategic aspects in isolation, which limits their applicability in complex decision-making scenarios. The proposed model, on the other hand, combines predictive analysis with functional segmentation of the building stock and comparative economic analysis considering the main profitability indicators (ROI, NPV, and payback period). This makes it possible to estimate expected energy performance and assess the financial sustainability of interventions based on buildings' typological characteristics. Including socio-economic and asset dimensions, such as unequal access to retrofit measures and the link between energy efficiency and property value, enables a more comprehensive approach to the challenges of the energy transition that goes beyond mere technical optimization. In this sense, the model meets the need for integrated decision-making tools that can support public policies and planning strategies that are geared towards equity, sustainability, and efficiency. The economic results confirm that the most effective intervention varies depending on the cluster. For instance, wall insulation is particularly beneficial in \"Critical\" buildings, while smart systems offer attractive returns in \"Moderate-High\" contexts. This demon strates the need for adaptive approaches in redevelopment policies rather than applying standardized solutions. It is also noteworthy that interventions on occupancy or plant parameters are less effective in terms of consumption forecasting, which reinforces the importance of acting on the physical characteristics of the building. This finding aligns with LSTM energy forecasting models, which, despite incorporating occupancy data, recognize the pivotal role of the building envelope. Although the analysis was conducted on a sample of buildings in the Lazio region, the proposed model has characteristics that facilitate its transfer to different climatic and regulatory contexts. The selection of standardized architectural and technological variables, the use of non-parametric algorithms such as random forest, and the adoption of generalizable economic metrics all support its applicability in heterogeneous scenarios. Furthermore, the approach has been designed to accommodate different levels of detail, which is important for adaptation in areas where data is limited or building characteristics differ. The use of open formats and the focus on replicability further strengthen the model's potential as a decision-making tool at a national or international level. To further clarify the model's generalizability, it is important to note that the variables used in both the predictive and clustering stages, such as wall and glass transmittance (U-values), surface-to-volume ratio, occupancy density, and HVAC system age, are not location-specific and are widely recognized as key factors in energy performance regardless of geographic context.