Weixuan Gao

Weixuan Gao is a Ph.D. candidate in Civil and Environmental Engineering at Stanford University.

His research interests include big data for extreme event, power system planning, machine learning and deep learning.

 

He received the B.E. degree in Statistics from Wuhan University, Wuhan, China, and the M.S. degree in Statistics from Washington University in St. Louis.

 

Publications:

• Probabilistic modeling for optimization of resource mix with variable generation and storage (W. Gao and D. Gorinevsky), IEEE Trans. on Power Systems, 2020
• Probabilistic planning of minigrid with renewables and storage in Western Australia (W. Gao, D. Tayal, and D. Gorinevsky), IEEE PES GM, 2019
• Probabilistic balancing of grid with renewables and storage (W. Gao and D. Gorinevsky), IEEE PMAPS, 2018