单 位：自动化学院
报告题目：How to make a building smart?
主讲人：Dr. Norman Chung Fai, Tse
时间：2017年1月19日（星期五）15:00-16:00
地点：工学二号馆219
个人简介：Norman Chung-Fai Tse received the degree from The Hong Kong Polytechnic University, Hong Kong, in 1985, the M.Sc. degree from The University of Warwick, Coventry, U.K., in 1994, and the Ph.D. degree from City University London, London, U.K., in 2007.
He is currently with the Centre for Smart Energy Conversion and Utilization Research, City University of Hong Kong, Hong Kong. His current research interests include power quality measurement and analysis, Web-based power quality monitoring, harmonics mitigation, and building energy efficiency study
报告内容：Major energy consumption of building systems such as lighting, HVAC and vertical transportation are dependent on building occupancy. Therefore occupancy-based control of building systems has Studies have revealed that a saving of up to 56% in HVAC systems can be obtained byloading occupancy-driven operation. Occupancy level is not only a significant parameter for building systems control, but also serve as a predictor of building energy consumption, And, one of the major attributes in building energy analysis. However, precise and reliable estimation of occupancy remains a challenge. Current detection technologies suffer from sensor drift, privacy concern, poor quality, intrusiveness, change of use and insufficient commissioning. More reliable and robust building occupancy sensors, alternative sensing technologies, and fusing techniques involving multiple environment sensors with varying accuracies have been proposed by researchers in recent years. Sensor fusion makes use of salient features of various sensors that can keep errors within control, thus providing better occupancy estimation possibilities. A robust multi-sensor fusion algorithm for occupancy detection in buildings may enable a more efficient building systems control and operation, and provide an alternative approach to building energy usage prediction and analysis. This talk hence addresses current researches done by the author on using Wi-Fi connection tracking techniques,computer vision and intelligent sensing algorithm to estimate occupancy level of a building, and how building users behave in terms of electricity utilization.
