Using Large Language Models to Support Utility-Scale Capacity Expansion Inputs

Modeling to support utility scale resource planning is data intensive; the sourcing, organizing, processing, and analyzing of needed data can be challenging and time consuming. Advancements in Artificial Intelligence (AI) tools, such as Large Language Models (LLMs) can boost researchers’ efficiency and accuracy working on such tasks. LLMs are text based predictive models trained to input and output natural language, code, and data. By using LLMs, energy system inputs such as forecasted demand, existing and planned generators, and/or fuel prices can be sourced or developed. Further, processing this data with the help of LLMs can help to develop the code and analysis to input such data into generalized data structures that work across tools. This report explores the practical application LLMs as coding partners for developing inputs to capacity expansion models, focusing on hands-on tasks where LLMs supported coding and data preparation.