Residential energy models can generate the kind of data most humans run from: thousands of buildings, dozens of columns, and at least 8,760 rows per column. Great for research, but difficult for anyone who just wants to ask, “What happens to electricity demand in Texas if homes used solar water heating?” or “How do air conditioning upgrades change my annual cooling costs in North Carolina?” Join us for this session as a University of Texas energy researcher and a Red Hat engineer team up to see what large language models can realistically do with this kind of messy, domain-heavy data using Python. We’ll show how we sample, reshape, and describe large datasets so LLMs can help generate and refine pandas/DuckDB queries, explain upgrade scenarios in plain English, and guide non-experts through “what if” electrification questions. This and more, all while being honest about where the models break down and why humans still need to do the science.

Session Speakers

Andy Igdal

PhD Student, Energy Policy, UT Austin

Cedric Clyburn

Senior Developer Advocate, Red Hat