# A Data-Driven Model for the Energy and Economic Assessment of Building Renovations
## 1 Introduction
### State of the Art  
The increasing accessibility of energy data and the advancement of digital technologies have led to the creation of a variety of tools and models for analyzing the energy performance of buildings. Methods based on building energy modeling (BEM), dynamic simulations, and decision support systems have been widely adopted for estimating the benefits of retrofit interventions [7; 8]. Meanwhile, the introduction of artificial intelligence and machine learning (ML) techniques has opened up new possibilities for automatically processing large amounts of data and constructing predictive models that are more flexible and adaptable than traditional deterministic approaches [9]. Despite these advances, the transition from experimental or specific tools to truly operational and scalable models remains limited. Many applications focus on specific case studies with ad hoc data that is difficult to replicate on a territorial scale [10; 11]. [PERSON] et al. propose an advanced method combining radial basis function neural networks (RBFNNs) and model predictive control (MPC) to enhance the energy efficiency of HVAC systems in sustainable buildings. When applied to residential and commercial buildings in Saudi Arabia, the model uses new input and output parameters to optimize performance and achieves a 15% reduction in energy consumption compared to conventional controls. The predictions proved accurate, with an error margin of between 0.2% and 2.5%, and the system ensured more precise control of thermal dynamics and variable loads [12]. [PERSON] evaluates the effectiveness of six ML models for predictive maintenance in building systems using a high-resolution dataset collected in six offices at Aalborg University (Denmark). Of the analyzed models, XGBoost demonstrated the greatest effectiveness (95% accuracy and an F1-score of 0.93), successfully predicting over 100,000 historical failures in HVAC, lighting, and occupancy systems. Notably, it identified critical issues such as lights being left on in unoccupied rooms and open shutters in empty spaces. These results highlight the potential of XGBoost for real-time fault detection and energy optimization in building management systems [13]. [PERSON] et al. developed an integrated artificial-intelligence-based framework to support multicriteria decision making in the energy retrofitting of existing buildings, combining ML surrogate models and evolutionary generative algorithms. Among the four ML models analyzed, LightGBM demonstrated the greatest accuracy and efficiency. By applying a heuristic algorithm and an entropy-weighted method, the system achieved average energy savings of 56.62%, a 51.60% reduction in emissions, and a 24.27% decrease in life cycle costs. The framework demonstrates the effectiveness of integrating AI and evolutionary optimization for sustainable and cost-effective energy retrofit strategies [14]. The study explores the prediction of energy consumption in commercial office buildings using an LSTM model that integrates time series electricity consumption data and occupancy information. Using real data collected at one-minute intervals from a building in Houston, USA, the model predicted energy consumption at different timescales (hourly, daily, and quarterly) for individual devices and the entire building. Evaluating the results using MAPE, MAE, and RMSE shows superior performance, highlighting the importance of occupancy in predictive models for improving energy efficiency by up to 20% [15]. [PERSON] et al. propose a three-phase framework for integrating ML into the architectural design process at the neighborhood scale to optimize energy efficiency from the outset. The focus is on Phase 1, where the CatBoost model is used to predict energy consumption with minimal inputs, which are typical of preliminary design. When applied to a New York case study, the model achieved high accuracy (R\({}^{2}\) = 0.88), confirming the validity of the selected key parameters.