The current UT microgrid system will become outdated as UT adopts renewable energy sources, especially as climate change prompts more severe weather changes. The variability of renewable energy sources presents an opportunity to develop an intelligent system that can preemptively detect potential blackouts. Our project aims to distribute power equitably and prevent long-term power failure in microgrids reliant on renewable energy. This is done by using machine learning to predict the demand and supply for an entire microgrid system and using that information to implement rolling blackouts.

Team Members:

Nora Agah, Trevor Liu, Kara Olson, Neeley Pate, Manthan Upadhyaya, Evan Varghese