Improving reliability in rural microgrids through AI-powered predictive maintenance: A critical review of technology and community readiness

Abstract

Reliable energy access remains a significant concern in rural Southeast Asia, particularly in off-grid communities marked by insufficient infrastructure, challenging geographic conditions, and limited technical expertise. Although renewable-energy microgrids, such as solar, biomass, hybrid photovoltaic-battery systems, and small hydropower, are increasingly deployed to reduce electrification gaps, inadequate maintenance strategies threaten the long-term reliability and sustainability of these systems. This paper critically examines emerging developments in artificial intelligence (AI)–driven predictive maintenance (PdM), emphasizing machine learning approaches such as long short-term memory (LSTM) networks, random forests, and hybrid ensemble models, as well as supporting technologies including digital twins, edge computing, and the Internet of Things (IoT). A narrative synthesis of peer-reviewed literature from 2020 to 2025 evaluates the technological feasibility, contextual adaptability, and implementation challenges associated with these innovations in resource-constrained rural environments. Special attention is given to sociotechnical barriers, including limited digital literacy, environmental stressors, inconsistent connectivity, and cultural perceptions of automated decision-making. The review highlights the importance of community readiness, participatory governance, and localized technician training as essential enablers of successful PdM adoption. Research gaps are identified in dataset availability, field validation, user-centered interface design, and the integration of policy frameworks that support sustainable deployment. Findings suggest that flexible PdM approaches combining real-time monitoring, lightweight AI models, and community capacity-building initiatives can significantly improve microgrid reliability and reduce maintenance costs. This paper contributes to ongoing discussions on rural electrification by linking predictive maintenance to broader human-centered design principles and inclusive innovation strategies. The insights presented may assist engineers, policymakers, and energy planners in deploying scalable, socially integrated PdM solutions that advance Sustainable Development Goals 7 (Affordable and Clean Energy) and 9 (Industry, Innovation, and Infrastructure), thus fostering equitable and sustainable energy access across underserved rural communities.