Systems oriented review on artificial intelligence as a sustainability enabler in renewable energy modeling and energy transition pathways

Artificial intelligence (AI) has emerged as a critical enabler of renewable energy system development, offering new capabilities for forecasting, optimization, predictive maintenance, and real-time control. However, existing review studies remain fragmented across individual renewable-energy technologies or algorithmic domains, limiting the transferability of knowledge and the development of system-level insights. Unlike traditional, purely technical algorithm reviews that primarily evaluate computational performance in isolation, this review adopts a systems-oriented framework that examines AI techniques according to their operational functions, deployment considerations, and contributions to long-term sustainability outcomes. Through a structured synthesis of AI applications across solar, hydropower, and wind energy systems, this study examines machine learning, deep learning, reinforcement learning, fuzzy logic, and hybrid AI–physics frameworks. The review evaluates AI techniques across key functional roles, including forecasting, optimization, maintenance, and control, while considering deployment factors such as scalability, interpretability, computational requirements, and implementation readiness. Crucially, the analysis connects these operational capabilities with broader sustainability outcomes, including renewable-energy integration, resource efficiency, system resilience, and decarbonization. The synthesis indicates that deep learning approaches dominate forecasting applications and achieve substantial improvements in predictive performance, whereas reinforcement learning and hybrid AI–physics models demonstrate strong potential for adaptive control, digital twins, and intelligent energy management. Across renewable-energy applications, hybrid frameworks provide a balanced trade-off among predictive accuracy, transparency, and practical deployment feasibility. By framing AI as a sustainability-enabling technology rather than merely a computational tool, this study provides a systems-oriented perspective that connects AI innovation with sustainable energy transitions and offers a practical roadmap for researchers, policymakers, power system engineers, and energy planners pursuing intelligent, resilient, and decarbonized renewable-energy infrastructures.