题目：Energy Efficiency Solutions in Smart Building Design using Machine Learning Technology

报告人：严柯 新加坡国立大学助理教授、博士生导师

时间：2021年6月4日（周五）下午15:00

地点：电气楼308

对象：565net必赢客户端研究生和教师，欢迎感兴趣的全校师生

主办单位：565net必赢客户端

报告人简介：

Ke Yan is currently an assistant professor with the department of Building, School of Design and Environment, National University of Singapore (NUS). He is also a visiting professor of the Waseda University, Tokyo, Japan and Huaqiao University, China. Dr. Yan is largely engaged in cross-discipline research fields, including machine learning, artificial intelligence, cyber intelligence, applied mathematics, sustainability and applied energy. He is actively involved with multiple highly-ranked journals’ editorial boards, such as the IEEE Transactions on Industrial Informatics (TII) and IEEE/ACM Transactions on Computational Biology and Bioinformatics. He has published more than 70 full length papers with highly ranked conferences and journals, including Association for the Advancement of Artificial Intelligence (AAAI), IEEE Transactions on Industrial Informatics (TII), IEEE Transactions on Sustainable Energy (TSE), IEEE Transactions on Systems, Man and Cybernetics: Systems (SMCA) and Applied Energy (AE).

报告简介： Data-driven energy efficiency solutions are highly demanded and among the most important topics in the related fields, such as smart building/city design, applied energy applications, electrical and electronic engineering, automation and constructions. Three topics are covered in this seminar, including: 1) data-driven fault detection and diagnosis (FDD) of heating, ventilation and air-conditioning (HVAC) systems, 2) energy consumption forecasting problem for individual households and 3) solar energy PV system optimization problems. In this seminar, first, the most up-to-date energy efficiency problems of the above three topics and the motivations of using machine learning techniques to solve these problems will be covered. Second, the methodologies that we proposed and developed in the past five years will be briefly described, which include semi-supervised learning, generative adversarial networks, long-short term memory and hybrid deep learning neural networks. Last, the trends of using machine learning technology in the field of building are summarized. Some on-going projects and future works of the presenter will also be introduced.