# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 5 Conclusions  
Within the framework of European climate neutrality and emission reduction targets, retrofitting existing buildings emerges as a key strategic lever. However, identifying truly effective and sustainable solutions is difficult due to the wide variety of building types, the lack of standardized predictive tools, and the economic uncertainty associated with interventions. Addressing these challenges, this research has developed and tested a data-driven digital methodology to inform decision making in retrofitting. The methodology consists of four main phases. First, a statistical and correlation analysis was conducted on a large dataset of residential buildings to identify the most relevant architectural, technological, and dimensional variables for energy consumption. Second, an ML model (random forest regressor) was applied to quantify the relative importance of each parameter and build a predictive system capable of accurately estimating the annual energy demand of buildings (R\({}^{2}\) = 0.91). The results confirmed the central role of the building envelope, particularly the transmittance of walls and glazed surfaces, as well as the percentage of window area. Thirdly, a k-means clustering phase was carried out on a subset of high-consumption buildings to define homogeneous performance digital representative building and guide targeted intervention strategies. Three main groups were identified: \"Critical\", \"High\", and \"Moderate-High\" buildings, each characterized by a specific combination of building parameters and energy performance. Finally, a comparative economic analysis was carried out for each cluster on five energy renovation measures: wall insulation; replacement of windows; upgrading of HVAC systems; installation of photovoltaic panels; and smart systems. Using real data on costs and expected benefits from regional technical sources, three key indicators were calculated: NPV, ROI, and payback period. This analysis revealed that the economic effectiveness of the measures varies significantly depending on the building profile, thus confirming the need for customized retrofit strategies.  
In conclusion, this study has demonstrated that an integrated approach based on real data, predictive algorithms, and economic modeling can effectively support the energy transformation of the building stock. The proposed model provides a solid basis for making objective, transparent, and replicable intervention decisions, with the potential to positively impact both energy efficiency and financial sustainability.  
Conceptualization, G.P. and F.M.; methodology, G.P., F.M. and Z.Z.; software, G.P., F.M. and Z.Z.; validation, G.P., F.M., and Z.Z.; formal analysis, G.P., F.M. and Z.Z.; investigation, G.P, F.M. and Z.Z.; resources, G.P., F.M. and Z.Z.; data curation, G.P., F.M. and Z.Z.; writing--original draft preparation, G.P., F.M. and Z.Z.; writing--review and editing, G.P., F.M., and Z.Z.; visualization, G.P., F.M. and Z.Z.; supervision, G.P.; project administration, G.P. All authors have read and agreed to the published version of the manuscript.  
The work was funded by Sapienza University Research Call 2023--CUP B83C23006090005, project title: Innovative Technological Solutions and Strategic Approaches to Enhance Energy Efficiency in Energy Production, Distribution and Utilization (SAEED); research topic: Optimization process for energy efficiency in buildings through the modelling, setting and monitoring of a Digital Twin and its integration of smart devices.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.