Analyze the unique challenges and opportunities presented by the integration of AI and machine learning within modern power systems, particularly in the context of a transitioning energy landscape.
Apply foundational concepts of data analytics, high-dimensional spaces, and sustainable computing practices to effectively model, optimize, and operate contemporary power systems and data center networks.
Synthesize knowledge of first-principle models, data-driven approaches, and sustainable AI strategies to develop innovative solutions for real-time power system operation while considering both performance and environmental impact.
This short in-person professional education course provides a comprehensive introduction to AI and machine learning tailored for power engineering applications, with an emphasis on sustainable computing practices. As the electricity industry transforms into a flat, active, and cyber-physical system, this course bridges domains to address challenges and opportunities, advancing smarter grids and sustainable AI systems.
Participants will gain hands-on experience with tools for statistical time series analysis, dimensionality reduction, and energy-efficient AI solutions. We will explore the differences between first-principle models, data-driven models, and sustainable AI strategies in real-time operations, with discussions and computer-based simulation projects. These activities will help participants integrate data-driven and physics-based reasoning while considering the environmental impact of AI and computing infrastructures.
Power Systems and Artificial Intelligence: An Introduction
