Machine Learning Based Non-Intrusive Load Monitoring (NILM) System for Household Appliances Usage Analysis

I&T Solution	Machine Learning Based Non-Intrusive Load Monitoring (NILM) System for Household Appliances Usage Analysis (REF : S-0007)
Trial Project
Solution Feature	Develop a Non-Intrusive Load Monitoring (NILM) system to identify the energy profile of household appliances Derive electricity consumption data of electrical appliance by using load signature and machine-learning technology Easy monitoring of energy consumption profile by end-users
Info on I&T Solution Provider	Solution Provider : City University of Hong Kong Address : Division of Building Science and Technology, College of Science & Engineering, City University of Hong Kong, Tat Chee Avenue, HK Contact Person : Dr. TSE Chung-fai Norman Position : Professor Tel : 3442 9836 Fax : 3442 9716 Email : bsnorman@cityu.edu.hk Webpage : www6.cityu.edu.hk/bst/staffprofile/norman_tse.htm

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