Smart renewable energy systems: the role of artificial intelligence in enhancing efficiency and reliability

Engineering notes

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Original abstract

The global transition toward renewable energy is essential for mitigating climate change, enhancing energy security, and achieving sustainable development. However, the large-scale integration of renewable energy sources, particularly solar and wind, introduces significant operational challenges due to their inherent variability, uncertainty, and decentralized characteristics. These challenges affect forecasting accuracy, grid stability, maintenance planning, and overall system efficiency, necessitating advanced analytical and control strategies. This review critically examines the role of Artificial Intelligence (AI) as a system-level enabler for enhancing the efficiency, reliability, and resilience of smart renewable energy systems. Unlike existing domain-specific reviews, this study provides a cross-domain synthesis of AI applications across key functional areas, including renewable energy forecasting, smart grid optimization, predictive maintenance, energy storage management, and operational decision-making. The analysis integrates recent advances in machine learning, deep learning, neural networks, and reinforcement learning, highlighting their capability to model complex nonlinear relationships and support adaptive system control. Empirical case studies in solar power forecasting and wind farm operation are evaluated to demonstrate measurable performance improvements, including reductions in forecasting error, enhanced energy capture, and improved operational efficiency. However, the findings also reveal that these improvements are highly dependent on data quality, model generalization, and system integration, and may not be directly transferable across different environments. The review further identifies critical barriers to large-scale deployment, including limitations in data availability, cybersecurity risks, computational complexity, and evolving regulatory frameworks. Emerging research directions, such as Edge AI, hybrid physics-based and data-driven models, AI-enabled microgrids, and advanced cybersecurity architectures, are examined as potential solutions to these challenges. Particular emphasis is placed on unresolved technical bottlenecks, including model interpretability, transferability, real-time deployment constraints, and the need for large-scale field validation. Overall, this review demonstrates that AI has significant potential to transform renewable energy systems, but its effectiveness depends on the integration of data-driven intelligence with physical system constraints, robust infrastructure, and supportive policy frameworks. The insights presented provide a structured foundation for researchers, industry practitioners, and policymakers seeking to develop scalable and reliable AI-driven solutions for next-generation smart renewable energy systems.

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