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 Zach O'Hagan, the CPO and GTM & Growth Lead at Zerve AI, 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.
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.
