# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 1 Introduction
### State of the Art
The results showed limited discrepancies compared to a traditional physical model (\(-\)8.69% to +11.04%), highlighting the potential of ML in supporting sustainable decision making from the earliest design stages [16]. [PERSON] et al. conducted an in-depth review of the integration of data-driven methods in energy retrofit strategies, highlighting the emerging role of explainable AI (XAI) in improving transparency and stakeholder adoption. The work emphasizes the importance of AI techniques for managing incomplete data, optimizing energy performance, and predicting the effects of interventions more accurately, while also suggesting future directions such as the integration of future climate scenarios and life cycle impact assessment [17]. [PERSON] et al. proposed a hybrid methodology, combining physical models and data-driven approaches, to evaluate energy retrofit solutions in different climates in the United Kingdom. The system is based on an automated dataset validated by IoT sensors and used to train ML models. The results highlight the effectiveness of air source heat pumps in cold-humid climates and WSHPs in harsher areas, suggesting climate-specific strategies to optimize energy efficiency and comfort [18]. [PERSON] et al. introduced the concept of the retrofit optimization problem (ROP), based on the multidimensional knapsack problem, to analyze the socio-economic sustainability of retrofit interventions in the Italian context. The model highlights strong inequalities: many energy-efficient strategies are inaccessible to low-income households, raising issues of equity in the energy transition and highlighting the need for targeted and income-sensitive policies [19]. [PERSON] et al. analyzed the impact of energy retrofits on the market value of multifamily residential buildings in Europe, integrating the cost approach with the contingent valuation method (CVM). The results show that refurbished buildings can achieve a market premium of 13.5%, demonstrating a direct correlation between energy efficiency and perceived property attractiveness [20]. However, most existing models primarily focus on energy aspects, neglecting an integrated assessment of intervention costs, return on investment, and the economic sustainability of different retrofit strategies. This limits their effectiveness as tools for supporting complex decisions, which necessarily require a balance of environmental requirements, technical constraints, and financial resources. As noted by [PERSON] et al. [21], data-driven tools such as digital twins can support sustainability by enabling informed, real-time decisions that integrate environmental, technical, and economic dimensions. Another limitation is the poor interoperability of data: information on building characteristics, actual energy consumption, intervention costs, and available incentives is often fragmented, incomplete, or difficult to compare across different contexts. This fragmentation hinders the development of generalizable, large-scale, predictive tools that can provide effective guidance even in the absence of detailed information on individual buildings. The lack of open, standardized formats for data collection, processing, and exchange is another obstacle [22]. Adopting open data and interoperable formats (e.g., based on shared ontologies or semantic protocols) would enable integration between different information sources, improve the reliability of analyses, and facilitate the replicability of models in heterogeneous territorial contexts. The literature review reveals a variety of approaches, some of which diverge, to identifying the most relevant parameters for defining redevelopment strategies. Some studies highlight the importance of geometric and morphological building characteristics, while others focus on plant engineering or behavioral aspects [23; 24]. This lack of consensus reflects the complexity of the phenomenon and the need to develop methodological approaches based on statistical evidence derived from large, representative samples.