Improving energy performance in existing buildings represents a critical challenge in the global transition toward sustainability and climate neutrality. Traditional retrofit strategies often rely on static models and expert-driven decision-making, which may fail to capture the dynamic and complex interactions between building systems, environmental conditions, and occupant behavior. This study investigates the application of machine learning approaches for detecting optimal interventions and operational conditions aimed at enhancing energy utilization in established facilities.

Adopting a research-oriented analytical framework, this study integrates qualitative synthesis of existing optimization models with a conceptual exploration of machine learning techniques, including artificial neural networks, hybrid intelligent systems, and multi-objective optimization algorithms. Drawing on existing literature in building energy performance, retrofit strategies, and computational modeling, the research develops a structured perspective on how data-driven approaches can improve decision-making processes in energy retrofitting.

The findings indicate that machine learning models offer significant advantages in identifying non-linear relationships, predicting energy consumption patterns, and optimizing retrofit scenarios under multiple constraints. Hybrid approaches that combine evolutionary algorithms with neural networks demonstrate superior performance in balancing energy efficiency, cost, and environmental impact. However, challenges persist in terms of data availability, model interpretability, and integration with existing building management systems.

The study contributes to the field by proposing a conceptual framework that integrates machine learning techniques with multi-criteria decision-making models for building retrofits. It highlights the importance of adaptive learning systems capable of continuous improvement based on real-time data. Furthermore, it identifies key limitations and suggests directions for future research, including the need for standardized datasets and improved model transparency.

Overall, this research underscores the transformative potential of machine learning in enhancing energy efficiency in existing buildings, offering a pathway toward more intelligent and sustainable infrastructure systems.