AI-Enhanced IoT Systems for Predictive Maintenance and Affordability Optimization in Smart Microgrids: A Digital Twin Approach
This addresses reliability and cost issues for distributed microgrid operators, though it appears incremental by combining existing technologies in a new application.
The study tackled predictive maintenance and affordability optimization in smart microgrids by integrating AI, IoT, and Digital Twin modeling, resulting in improved predictive accuracy, reduced downtime, and cost savings compared to baseline methods.
This study presents an AI enhanced IoT framework for predictive maintenance and affordability optimization in smart microgrids using a Digital Twin modeling approach. The proposed system integrates real time sensor data, machine learning based fault prediction, and cost aware operational analytics to improve reliability and energy efficiency in distributed microgrid environments. By synchronizing physical microgrid components with a virtual Digital Twin, the framework enables early detection of component degradation, dynamic load management, and optimized maintenance scheduling. Experimental evaluations demonstrate improved predictive accuracy, reduced operational downtime, and measurable cost savings compared to baseline microgrid management methods. The findings highlight the potential of Digital Twin driven IoT architectures as a scalable solution for next generation intelligent and affordable energy systems.