A Systematic Review of Artificial Intelligence Applications in Renewable Energy Systems

Abstract

The rapid Progress toward sustainable energy development has advanced the implementation of Artificial Intelligence (AI) to address the operational, technical, and economic challenges associated with renewable energy integration. This systematic review provides a comprehensive analysis of recent advances in the application of artificial intelligence to renewable energy systems, with a focus on machine learning, deep learning, reinforcement learning, explainable AI, and hybrid optimization approaches. The review follows a structured systematic methodology based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, covering peer-reviewed studies published between 2020 and 2026 from major scientific databases. The findings reported across the reviewed studies highlight that AI now serves as a transformative technology across multiple renewable energy domains. AI-driven models significantly enhance renewable energy forecasting, predictive maintenance, fault diagnosis, energy management, load prediction, demand response, and system optimization, resulting in improved operational efficiency, reliability, and cost-effectiveness. Deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, have demonstrated remarkable accuracy in forecasting renewable energy generation. Additionally, reinforcement learning and metaheuristic optimization techniques facilitate adaptive energy management and improve resource allocation efficiency. Emerging technologies, including digital twins, federated learning, edge AI, generative AI, and explainable AI, are further expanding the capabilities of intelligent renewable energy systems. Despite notable advancements, several issues continue to hinder progress, such as the scarcity of reliable datasets, concerns related to cybersecurity and data privacy, limited transparency of predictive models, high computational requirements, difficulties in integrating with existing infrastructure, and the absence of universally accepted benchmarking standards. This review highlights major research gaps and outlines potential directions for future investigations, with a particular emphasis on trustworthy AI, physics-informed machine learning, multimodal data fusion, autonomous energy management systems, and AI-enabled carbon-neutral energy infrastructures. The results indicate that artificial intelligence can significantly contribute to the development of resilient, intelligent, and sustainable renewable energy systems. This review provides researchers, engineers, policymakers, and industry practitioners with a comprehensive understanding of current developments and opportunities at the intersection of artificial intelligence and energy technologies.