Machine learning and digital twin integration for predictive monitoring of urban critical energy infrastructure

The increasing complexity of urban energy infrastructure, coupled with growing requirements for reliability, resilience, and operational efficiency, has accelerated the adoption of advanced monitoring technologies capable of supporting predictive decision-making. Traditional supervisory systems primarily provide information regarding the current operating state of infrastructure assets and often lack the capability to anticipate future operating conditions, equipment degradation, and emerging system vulnerabilities. Recent advances in digital twin technology and machine learning have created new opportunities for developing intelligent monitoring platforms that combine physics-based modeling with data-driven analytics to improve situational awareness and support proactive asset management. This work presents an integrated framework for predictive monitoring of urban critical energy infrastructure based on the combination of machine learning techniques and digital twin technology. The proposed approach utilizes a continuously synchronized digital representation of physical energy assets that receives real-time operational data from field devices, communication networks, and supervisory control systems. The digital twin environment reproduces the operational behavior of the monitored infrastructure while enabling simulation of future operating scenarios and assessment of potential system responses to disturbances and equipment degradation processes. Machine learning algorithms are integrated into the digital twin framework to enhance condition assessment, anomaly detection, fault identification, and predictive analytics capabilities. The proposed architecture combines real-time measurements, historical operational records, and simulation-generated data to create a unified analytical environment capable of identifying hidden patterns associated with infrastructure health and system performance. The integration of data-driven intelligence with physics-based models enables more accurate prediction of equipment condition and supports the early detection of abnormal operating states. A digital substation-based experimental platform is employed to validate the proposed methodology and investigate the interaction between physical infrastructure, digital twin models, and machine learning modules. The developed framework supports continuous monitoring, predictive maintenance planning, reliability assessment, and decision-support functions for operators responsible for managing urban energy systems. Experimental investigations demonstrate the feasibility of integrating machine learning and digital twin technologies within a unified cyber-physical environment for critical infrastructure monitoring. The obtained results indicate that the proposed approach significantly enhances the analytical capabilities of conventional monitoring systems by enabling predictive assessment of infrastructure behavior and proactive management of operational risks. The developed framework contributes to the advancement of intelligent energy management technologies and provides a scalable foundation for future smart grid applications, resilient energy systems, and AI-supported critical infrastructure protection.