Data-Driven Building Retrofits: Predicting Energy Savings with AI for Sustainable Project Management

Achieving the European Union’s climate and energy targets requires large-scale improvements to the performance of existing residential buildings. Traditional methods for assessing retrofit strategies, although accurate, are often time-consuming, data-heavy, and unsuitable for early decision-making. This pa- per introduces XGreen, a predictive tool designed to estimate the outcomes of retrofit interventions in residential buildings in Lombardy, Italy, using only lim- ited and readily available data from energy performance certificates (EPCs). The study investigates whether regional EPC records can be used to provide reliable predictions of two key outcomes: non-renewable energy performance (EPgl,nren) and the achievable post-retrofit energy class. The tool was developed using a curated dataset of more than two million entries from Lombardy’s CENED 2.0+ register. Fourteen models based on the XGBoost algorithm were trained: seven regressors to predict post-retrofit EPgl,nren, and seven classifiers to estimate the final energy class, using the regressor outputs as inputs. Results show that the regressor associated with transparent envelope up- grades achieved the best performance, with a mean absolute percentage error of 6.9%. All regressor errors were within the lowest 3% of their respective EPgl,nren distributions, indicating high reliability. Among the classifiers, the combined-intervention model achieved the strongest results, with 88.3% accu- racy in predicting buildings upgraded to class A4. By providing rapid, data-driven feedback on different retrofit options without the need for full building simulations, XGreen supports faster, more cost-effective planning. The tool offers a scalable framework to guide policymakers, auditors, and owners in meeting EU renovation targets and advancing building decarbon- isation.