AI-Enhanced IoT Systems for Predictive Maintenance and Affordability Optimization in Smart Microgrids: A Digital Twin Approach

📝 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.

💡 Analysis

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.

📄 Content

The integration of IoT and AI technologies in smart energy systems is rapidly transforming the landscape of energy generation, distribution, and consumption. As the world transitions toward decentralized, sustainable energy models, microgrids and minigrids are emerging as crucial components for ensuring energy resilience and independence, particularly in remote and underserved areas [1], [2]. However, these systems face significant operational challenges, including the high cost of maintenance, unpredictable component failures, and a lack of real-time visibility, which collectively hinder their widespread adoption and long-term viability [3], [4].

Traditional maintenance approaches, such as reactive or time-based methods, are often inefficient, leading to costly downtime and reduced system lifespan. Recent studies have shown that conventional maintenance strategies result in up to 40% higher operational costs compared to intelligent predictive approaches [3], [5]. To address these issues, this proposal outlines a research project focused on a holistic, AI-driven framework that leverages IoT-based real-time data to create a predictive maintenance system [6], [7].

By adopting a digital twin approach, the research will not only optimize maintenance and reduce operational costs but also improve the overall affordability and accessibility of energy in local communities, thereby contributing to a more equitable and sustainable energy future [8]- [10]. The integration of cybersecurity measures from the ground up ensures system resilience against emerging threats in connected energy infrastructure [11]- [13].

The period from 2020 to 2025 has seen a paradigm shift in smart microgrid research, driven by advancements in IoT, AI, and digital twin technologies. This section summarizes recent research, identifying key contributions and emerging trends across these interconnected fields.

Recent studies in this area have moved beyond basic data collection to focus on creating more secure, decentralized, and efficient monitoring systems [1], [14]. Zhang et al. demonstrated how distributed IoT frameworks can significantly reduce maintenance costs while improving data integrity through advanced encryption protocols [1]. Researchers are exploring how IoT frameworks can be optimized with edge computing to reduce latency and enable real-time decision-making [15].

Low-cost IoT platforms and satellite-based communication are being developed to make microgrids viable in rural and remote settings, addressing challenges like connectivity and power consumption [16], [17]. Khan et al. showed that ESP32based systems with LoRa communication can achieve 99.2% uptime in rural deployments while maintaining costs below $200 per monitoring node [16]. Rahman et al. further demonstrated that satellite-based IoT systems can extend connectivity to areas previously considered unreachable for intelligent monitoring [17].

Advanced communication protocols including MQTT-SN for sensor networks, CoAP for constrained devices, and emerging 5G-enabled solutions have been extensively validated for microgrid applications [15]. These protocols address critical challenges related to network reliability, energy efficiency, and arXiv:2511.12175v1 [eess.SY] 15 Nov 2025 scalability in distributed energy systems while maintaining security standards appropriate for critical infrastructure.

The application of machine learning in predictive maintenance has grown significantly, with a focus on enhancing fault prediction accuracy and enabling dynamic scheduling [5], [6], [18]. Kumar et al. demonstrated that LSTM networks can achieve 94.7% accuracy in predicting solar inverter failures up to 14 days in advance [5]. Advanced models, such as LSTM networks and federated learning, are being used to analyze complex temporal data and ensure data privacy across distributed microgrids [18], [19].

Hybrid models that combine physics-based simulations with deep learning are emerging, offering improved accuracy and explainability of fault detection [6]. Gupta et al. showed that hybrid approaches can reduce false positive rates by up to 35% compared to purely data-driven methods while maintaining high sensitivity to actual faults [6]. Graph Neural Networks have demonstrated particular effectiveness in modeling the interconnected nature of microgrid components [7].

Multi-agent reinforcement learning systems have shown promise for coordinating maintenance activities across multiple interconnected microgrids [20]. Chen et al. demonstrated that multi-agent approaches can optimize maintenance scheduling across distributed systems while reducing overall costs by 28% and improving system availability by 15% [20].

Ensemble methods, including Random Forests, Gradient Boosting, and advanced voting classifiers, have been extensively studied for their ability to improve prediction reliability and quantify uncertainty in maintenance recommendations. These approaches are particularly va

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