The unprecedented growth of renewable energy infrastructure poses both technical and humanistic issues, especially about the reliability of the system, efficiency, and adaptability of the workforce. This literature review investigates the role of artificial intelligence (AI) in optimizing renewable energy systems (RES) through fault detection and predictive maintenance (PdM) from an industrial psychology perspective to understand human-technology interactions. The research reflects current studies on AI-powered monitoring devices, anomaly detection methods, and PdM models, and their usefulness in minimizing unexpected downtime, extending equipment life, and optimizing overall equipment performance. At the same time, the review highlights how industrial-psychological influences, including employee attitudes, cognitive preparation, and behavioral adaptation, can determine the successful adoption and use of AI-enabled maintenance solutions. It has been shown that the benefits of AI technologies, development of trust, and reduction of resistance to technological change are highly dependent on workforce engagement, training, and participation in decision-making. The review also establishes the major organizational, social, and economic consequences of incorporating AI into human-centric responses, indicating that the best opportunities are expected to arise from matching technical advances with human behavioral concerns. Lastly, existing research gaps are addressed, including empirical research on the long-term relationship between humans and AI, cross-cultural workforce strategies, and scalable implementation plans for various renewable energy infrastructure. Altogether, this review has demonstrated the prospective changes that can be made to the possibility of stabilizing the renewable energy infrastructure through the combination of PdM, AI-based fault detection, and industry-oriented psychological insights, providing a holistic framework to improve the stability, sustainability, and human-machine interactions of the renewable energy infrastructure.
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