Published April 1, 2019
| Version v1
Journal article
A Supervised Learning Approach to Appliance Classification Based on Power Consumption Traces Analysis
Creators
- 1. Department of Engineering Education, University of Mindanao – Tagum, Visayan Village, Tagum City, Davao del Norte 8100 (Philippines)
- 2. School of EECE, Mapua University, 658 Muralla St., Intramuros, Manila 1002 (Philippines)
Description
Electric appliances are everyday major power consumers. Management and control of these appliances can only be possible with appliance classification infrastructures. An appliance classification smart meter, with a provision for remote control, is developed based on time-dependent power features drawn by an appliance, from power-up to its steady state. The kNN classifier is highly accurate at 95.55% in classifying the appliance class. (paper)
Availability note (English)
Available from http://dx.doi.org/10.1088/1757-899X/517/1/012011Additional details
Identifiers
Publishing Information
- Journal Title
- IOP Conference Series. Materials Science and Engineering (Online)
- Journal Volume
- 517
- Journal Issue
- 1
- Journal Page Range
- [8 p.]
- ISSN
- 1757-899X
Conference
- Title
- 2. International Conference on Robotics and Mechantronics
- Dates
- 9-11 Nov 2018
- Place
- Singapore (Singapore)
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 52108041
- Subject category
- S42: ENGINEERING;
- Resource subtype / Literary indicator
- Conference
- Descriptors DEI
- CLASSIFICATION; ELECTRIC APPLIANCES; METERS; REMOTE CONTROL; STEADY-STATE CONDITIONS; TIME DEPENDENCE
- Descriptors DEC
- APPLIANCES; CONTROL; ELECTRICAL EQUIPMENT; EQUIPMENT; MEASURING INSTRUMENTS