报告题目：Data-Driven Multi-Model Blending for Renewable Energy Forecasting

报告人： Jie Zhang

Assistant Professor

Department of Mechanical Engineering

Department of Electrical Engineering (by courtesy)

University of Texas at Dallas

时间：2016年12月16日上午10:00

地 点：西主楼3-102

联系人：胡泽春

报告摘要：

Variable renewable energy resources such as wind and solar power are becoming increasingly important sources of energy on the electric power system.

The consistent growth of renewable energy calls for a paradigm shift in energy systems technologies, aiming to efficiently solve power systems challenges with large penetrations of renewable energy and energy efficiency technologies. Improving wind and solar forecasting accuracy becomes increasingly important to ensure economic and reliable operations.

This presentation will discuss several recently developed data-driven methodologies in wind and solar 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; and

(iii) a ramp identification and forecasting method for extreme events.

Both the economic and reliability benefits from improved wind and solar power forecasting will also be discussed.

报告人简历：

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, complex engineered systems, wind energy, power & energy systems, and renewable integration. This research has resulted in over 80 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.