Abstract: Explainable AI has been introduced to guide various industrial process involving complex human behaviours, which aims to interpret what , why and how the solution produced by deep learning system for engineering practitioners. This talk will present a research on how to achieve an explainable model by different techniques from a data-driven perspective. Moreover, how to apply XAI for energy system will be presented by the integration of renewable energy resource. Specifically, a large-scale efficient computational algorithm will be discussed while game theoretical models will be highlighted for challenging issues including demand side management, demand response of EV management, multi-energy trading mechanism design and distributed renewable energy integration in the smart grid.

Biography: Dr. Chaojie Li received the B.Eng. degree in electronic science and technology and the M. Eng. Degree in computer science from Chongqing University, China, in 2007 and 2011, respectively, and received the PhD. Degree from RMIT University, Australia in 2017, where he was a research fellow for one and a half years. He worked as a senior algorithm engineer at Alibaba group for one year. He is an ARC Discovery Early Career Researcher Award (DECRA) recipient. At present, He is a senior research associate at UNSW at Sydney. His current research interests include graph representation learning, distributed optimization and game theory in smart grid, big data analysis, and cyber security.