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清华大学 贾庆山 副教授学术报告

来源: 点击: 时间: 2018年09月10日 16:33

地点:升华后楼 504

时间:9月11日,早上 9:30-12:00

摘要和简历:

Title: Event-Based Learning and Optimization for Cyber-Physical Energy Systems

Abstract:

Cyber physical energy system (CPES) is where information and energy merges together to improve the overall system performance including economic, comfort, and safety aspects. Artificial intelligence which are enabled by internet of things, big data, and cloud computing, has a big role in the optimization of CPES. In this talk, we focus on a real problem in smart buildings, in which multiple buildings are connected into a micro grid. The renewable energy such as solar power and wind power are generated locally in the building, stored in the building, and consumed in the building by plug-in loads and electric vehicles. There are models to predict the power generation and consumption in minutes, hours, and days. And there are models to predict the power generation and consumption in individual buildings or a group of buildings. We developed a multi-scale event-based reinforcement learning method which makes decisions only when certain events occur, and uses policy projection and state and action aggregation to connect the models in multiple scales. The performance of this method is demonstrated by numerical examples. We will also discuss extensions of this method to distributed optimization. We hope this work sheds light to the optimization of CPES.

Speaker: Qing-Shan Jia, Associate Professor, Center for Intelligent and Networked Systems, Tsinghua University, Beijing, China.jiaqs@tsinghua.edu.cn

Short Bio:

Qing-Shan Jia received the B.E. degree in automation in July 2002 and the Ph.D. degree in control science and engineering in July 2006, both from Tsinghua University, Beijing, China. He is an Associate Professor in the Center for Intelligent and Networked Systems (CFINS), Department of Automation, Tsinghua University. He was a visiting scholar at Harvard University in 2006, at the Hong Kong University of Science and Technology in 2010, and at Laboratory for Information and Decision Systems, Massachusetts Institute of Technology in 2013. His research interest is to develop an integrated data-driven, statistical, and computational approach to find designs and decision-making policies which have simple structures and guaranteed good performance. His work relies on strong collaborations with experts in manufacturing systems, energy systems, autonomous systems, and smart cities. He is an associate editor (AE) of IEEE Transactions on Automatic Control, and was an AE of IEEE Transactions on Automation Science and Engineering (2012-2017) and Discrete Event Dynamic Systems – Theory and Applications (2012-2016). He served the Discrete Event Systems Technical Committee chair in IEEE Control Systems Society (2012-2015), and now serves the Control for Smart Cities Technical Committee chair in International Federation of Automatic Control, the Smart Buildings Technical Committee co-chair in IEEE Robotics and Automation Society, and the Beijing Chapter Chair of IEEE Control Systems Society. He is a member of the 11th Chinese Automation Association Technical Committee on Control Theory (2018-2022) and the 1st Chinese Automation Association Technical Committee on Information Security of Industrial Systems (2016-2020).


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