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

Solution Machine Learning Based Non-Intrusive Load Monitoring (NILM) System for Household Appliances Usage Analysis
(REF : S-0007)
Features of Products / Solutions
  • 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
Matched Trial Project To be arranged
Contact Information

Company: 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.

Name: 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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