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Use AI Agents to Analyze Power Grid Optimization

Tuesday, May 5, 11 AM ET

Key Takeaways

  • Explore how AI agents can be applied to real-world data analysis problems.
  • Build and analyze a synthetic power grid to understand load balancing and optimization.
  • Learn how to use Zerve AI’s notebooks and agent workflows for advanced data science tasks.

Your Presenter(s)

Greg Michaelson headshot

Greg Michaelson

CPO & Co-founder at Zerve AI

Greg Michaelson champions the perspective of Zerve’s users, helping ensure the platform reflects the realities of day-to-day working with data, and is an active contributor to the data science community. Previously, he was an early leader at DataRobot, serving as Chief Customer Officer after senior analytics roles at Travelers and Regions Bank. He began his career teaching statistics at the University of Alabama

Jason Hillary headshot

Jason Hillary

CTO & Co-founder at Zerve AI

Jason Hillary is co-founder and CTO of Zerve, an AI platform for research and analytics. He has spent over a decade building machine learning and data science systems and holds a PhD in applied mathematical approaches to energy efficiency, with a focus on simulation accuracy and neural networks applied to sensor data.

Why this matters

Balancing supply and demand in a power grid is a complex, high-stakes problem—making it an ideal use case for AI-driven analysis. By combining agentic workflows with modern data science tools, you can simulate, analyze, and optimize systems that would be difficult to manage manually.

In this code-along, Greg Michaelson and Jason Hillary, the

CPO and CTO at Zerve AI, will guide you through building and analyzing a synthetic power grid using Zerve AI’s data science notebooks and agentic workflows. You’ll explore how AI agents can assist with data analysis, optimization, and decision-making, while gaining hands-on experience with a realistic energy systems scenario. You'll generate a network of substations and transmission lines, simulate fluctuating demand, define generation costs and line capacity constraints, and optimize power flow across the network.

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