RU-WRF: A Physics-Guided Spatiotemporal Wind Speed Forecasting Model and Its Application to the U.S. North Atlantic Offshore Wind Energy Areas
Advisor: Dr. Aziz Ezzat Ahmed
Feng Ye received his B.S. degree in Fluid Mechanics and Automatic Control from Jiangsu University. He also received an M.Sc. degree in Mechanical and Electrical Engineering from Southeast University, China, in 2020. He is currently pursuing a Ph.D. in Industrial and Systems Engineering and MSc in Statistics at Rutgers-New Brunswick. He is a student member of INFORMS and IISE. Since joining Rutgers as a Ph.D. student in fall 2020, Feng has been directly involved in offshore wind energy research. The overall topic of his research work is to develop machine learning (ML) and artificial intelligence (AI) methods to improve the forecasting, operations and maintenance (O&M) of offshore wind energy, with a particular focus on the wind energy areas in the U.S. Mid and North Atlantic. He is working closely with collaborators from the Rutgers University Center for Ocean Observing Leadership (RUCOOL) to develop a physics-informed machine learning model for wind energy forecasting. Feng also leads another line of research in offshore wind related to data-driven fault detection and asset health monitoring for ultra-scale wind turbines.