Project Description
We use an interdisciplinary approach that aspires to balance the scales of fairness in energy systems. It demands fair access to clean energy, fair distribution of job opportunities in the renewable industry, and adequate protection from environmental harm for every community. Understanding the intersection of energy policy and social issues, therefore, becomes pivotal to achieving a just transition to renewable energy. The “AI-Powered Analysis of Renewable Energy Laws” project is rooted in this energy framework.
The primary objective of the project is to leverage large language models (LLMs) and high-performance computing (HPC) to interpret and score energy laws. The analysis will be based on the scorecard, focusing on accessibility, affordability, and environmental impact of each law. Participants will learn how to prompt the LLM to evaluate a bill based on this scorecard and then automate the workflow to download and score numerous bills. By automating this process with AI and HPC, we can efficiently analyze a vast array of bills, even those hundreds of pages long, in a fraction of the time it would take manually. Automation also minimizes human bias and error, increasing the consistency and reliability of the scoring process.
