AI-Powered Smart Energy Management for Optimizing Energy Efficiency in High-Performance Computing Systems

The chapter discusses the need for efficient energy consumption in high- performance computing systems and proposes the integration of artificial intelligence and machine learning techniques to optimize energy efficiency. It explores AI- driven techniques like reinforcement learning, neural networks, and predictive analytics for energy- aware scheduling, workload allocation, and adaptive power management. The chapter discusses the effectiveness of AI- driven energy optimization strategies in real- world HPC infrastructures, highlighting potential energy savings while maintaining computational performance. It also discusses future directions and challenges in AI- enabled smart energy management, including algorithm refinement, integration with emerging technologies, and scalability considerations. The holistic approach highlights the transformative impact of AI and ML in creating sustainable, energyefficient paradigms within high- performance computing ecosystems.