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)
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
Trial Application and Expected Outcome To be arranged
Info on I&T Solution Provider
Solution Provider:Division of Building Science and Technology, City University of Hong Kong
Address:Division of Building Science and Technology, College of Science & Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China.
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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