October 24, 2017, at the invitation of prof. Zhang Yunqing,Dr. Zhang Jie fromthe University of Texas at Dallas visited School of Mechanical Science and Engineering in the afternoon and gave a report named Data-Driven Multi-Model Blending for Renewable Energy Forecasting.
Inthis report, Dr Zhangdiscussed several recently developed data-driven methodologies in wind and solar energy forecasting, including: (i) improved wind power forecasting using big data information processing technologies, leading to significant production cost reductions in power system operations; (ii)a situation-dependent multi-expert machine learning solar forecasting methodology; (iii) a ramp identification and forecasting method for extreme events.
Dr. Jie Zhang is currently an Assistant Professor in the Department of Mechanical Engineering and (by courtesy) Department of Electrical Engineering at the University of Texas at Dallas (UTD). Before joining UTD, he was a Research Engineer at the National Renewable Energy Laboratory (NREL). Dr. Zhang received his Ph.D. (2012) in Mechanical Engineering from Rensselaer Polytechnic Institute (RPI), Troy, NY, USA. He received his B.S. (2006) and M.S. (2008) in Mechanical Engineering from Huazhong University of Science & Technology, Wuhan, China. His research expertise and interests are multidisciplinary design optimization, big data analytics, machine learning, complex engineered systems, wind energy, power & energy systems, and renewable integration. This research has resulted in over 100 peer-reviewed journal and conference publications. He has received the best paper awards from Renewable Energy journal and IEEE Power & Energy Society General Meeting. He is a senior member of IEEE and AIAA. He is a member of AIAA Multidisciplinary Design Optimization technical committee and ASME Solar Energy Division technical committee.
