Abstract
Ocean Renewable Energy (ORE) systems—comprising wind, wave, tidal, and ocean thermal energy—are increasingly seen as viable alternatives to fossil fuels. However, their integration into the power grid is hindered by environmental sensitivity, dynamic ocean conditions, and high maintenance demands. Artificial Intelligence (AI) offers promising solutions to these challenges by enabling intelligent, adaptive, and resilient energy systems. This review explores AI applications in ORE, focusing on three critical domains: optimization, forecasting, and control. Optimization techniques, including Genetic Algorithms (GA) and Swarm Intelligence (SI), are employed to enhance device efficiency, improve energy capture, optimize farm layouts, reduce environmental impacts, and lower installation costs. Forecasting uses Machine Learning (ML) and Deep Learning (DL) models to predict wave height, tidal flow, and energy output, aiding in grid integration and energy scheduling. In control systems, AI approaches like Reinforcement Learning (RL) and Fuzzy Logic ensure real-time responsiveness and predictive maintenance, improving system reliability in dynamic marine environments. Emerging technologies such as Edge AI enable decentralized computation for real-time decision-making, while Digital Twin frameworks simulate and predict system performance before deployment. Explainable AI (XAI) is also discussed to ensure transparent and trustworthy decision-making. Ethical and regulatory concerns are acknowledged to ensure responsible AI integration in ocean settings. Overall this review offers a comprehensive synthesis of how AI enhances the performance, efficiency, and scalability of ORE systems. It serves as a valuable resource for researchers, policymakers, and industry professionals seeking to advance clean, smart, and sustainable ocean energy solutions.