Artificial Intelligence Techniques for Efficient Control of Shipboard Power Systems: A Review

Maritime transport contributes approximately 2.5% to global greenhouse gas (GHG) emissions and faces rising operational costs due to increasing fuel prices. Optimizing shipboard energy systems has become essential to enhancing sustainability and efficiency. This paper presents a comprehensive review of artificial intelligence (AI), machine learning (ML), and deep learning (DL) methods applied to the optimization and control of ship microgrids. It highlights the architectures, challenges, and benefits of integrating AI into marine energy systems. A comparative analysis of AI-driven schemes for energy efficiency, fault diagnosis, and emission reduction is presented. The findings underline the transformative potential of AI-based control systems in enabling intelligent, adaptive, and environmentally compliant marine operations.