The project will use Generative Adversarial Network (GAN) to generate space-time correlated renewable generation scenarios. The student will gather historical wind speed and solar radiation data from Texas, and train a GAN to generate scenarios. The student will also investigate the scenario correlation with temperature, and use average temperature as a key feature for scenario generation, and benchmark it with alternative scenario generation approaches. This effort is part of a storage valuation project funded by DSI Seed Grant in 2022, in which these generated scenarios will be used for performing storage valuation.

This project is eligible for a matching fund stipend from the Data Science Institute. This is not a guarantee of payment, and the total amount is subject to available funding.

Faculty Advisor

  • Professor: Bolun Xu
  • Center/Lab: EEE
  • Electric power system decarbonization

Project Timeline

  • Earliest starting date: 3/1/2022
  • End date: 8/1/2022
  • Number of hours per week of research expected during Spring/Summer 2022: ~8
  • Number of hours per week of research expected during Summer 2022: ~20

Candidate requirements

  • Skill sets: Experience with ML and Python.
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes