Published April 1, 2019 | Version v1
Journal article

A Supervised Learning Approach to Appliance Classification Based on Power Consumption Traces Analysis

  • 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/517/1/012011

Additional details

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