Our project predicts profitable source-sink relations for Congestion Revenue Rights (CRR) auctions within the ERCOT Energy market, utilizing machine learning and diverse data sources. The software, developed with Python and Next.js, integrates natural language processing and historical data to assist users in informed CRR bidding decisions, impacting resource entities' strategies and potentially affecting consumer prices and market profitability.

Team Members

Ryan Ahmed
Eric Borrego
Nathaniel Delgado
Adal Ordonez
Nicholas Richards
Ilaan Siddiqi