# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 1 Introduction
### Research Objective  
Considering the critical issues that have emerged in the literature and the operational needs of the AECO sector, this study aims to develop an integrated, data-driven digital approach for the integrated assessment of energy renovation interventions in the built environment. In particular, the main objective of the research is to build a predictive and decision-making model based on the analysis of real data and aimed at supporting informed choices in terms of energy and economics. The methodological approach is divided into several phases. In the first phase, it is intended to analyze a large database of buildings, including construction, technological, climatic, and functional variables, in order to identify, through the use of ML algorithms, the parameters that have the most significant influence on the energy consumption of buildings [25]. The use of advanced ML techniques makes it possible to overcome the limitations of deterministic approaches and to capture complex and non-linear relationships between building characteristics and observed energy performance. Unlike many existing analyses, which focus on small datasets or individual case studies, this work aims to build a generalizable and adaptable model capable of returning reliable results even in different territorial contexts and with varying levels of detail. In this perspective, the adoption of open and interoperable data formats is a key element in ensuring the scalability and replicability of the methodology.  
The second phase of the research consists of associating the main energy retrofit measures, such as thermal insulation, replacement of windows and doors, and upgrading of heating systems, with an estimate of the average unit costs, obtained through the processing of technical databases, market reports, and documented case studies [26; 27]. By combining these costs with the energy benefits estimated in the previous phase, it will be possible to build a cost-benefit analysis model that provides summary indicators of economic viability, including the payback period and return on investment (ROI) [28].  
The main hypothesis guiding the research is that the joint analysis of large-scale building data, combined with predictive ML techniques and economic cost/benefit modeling, can generate a practical and effective tool to guide decisions on renovation interventions. This tool would enable public and private operators, designers, municipal technicians, investors, and policy makers to objectively and transparently assess the effectiveness of different retrofit strategies in relation to the specific context and the energy and economic objectives set. The expected output is therefore the definition of an operational methodology, based on flexible interpretative models and accessible data, which can be integrated into spatial planning processes and public policy support systems for ecological transition. Looking ahead, the scientific contribution aims to bridge the gap between the theoretical potential of digital tools and their actual application on an urban and territorial scale, promoting a culture of data-driven urban design and transformation.