AI-Enhanced IoT Systems for Predictive Maintenance and Affordability Optimization in Smart Microgrids: A Digital Twin Approach
Koushik Ahmed Kushal, Florimond Gueniat
TL;DR
The paper presents an AI-enhanced IoT framework for predictive maintenance and affordability in smart microgrids using a Digital Twin approach. By synchronizing real-time sensor data with virtual models, it enables early fault detection, dynamic load management, and optimized maintenance under cyber-physical constraints. The approach combines multi-objective optimization, open-source hardware, hardware-in-the-loop validation, and socio-economic analysis to demonstrate cost reductions and improved reliability. The work highlights the practical impact of Digital Twin driven IoT architectures for scalable, resilient, and affordable energy systems, particularly in underserved regions.
Abstract
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.