Feature extraction and classification using power demand information

Tomoya Imanishi, Rajitha Tennekoon, Hiroaki Nishi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

Electrical load monitoring, by means of a smart meter, is getting more and more popular these days. Power demand information from smart meters is drawing attention among researchers, since it could be applied for power demand control. Providing attractive services with smart meters encourage electricity retailers to utilize demand side management, which could be a solution for energy-related problems in our society. In this paper, a novel service is proposed by classifying private information from the household electricity usage. The private information is estimated using feature vectors extracted from time series analysis of power demand information. In order to extract feature vectors effectively, two extraction methods were proposed: simple statistical method, and Discrete Fourier Transform (DFT) based extraction method. Then, Support Vector Machines (SVMs) classifier is carried out after the optimization of hyper-parameters. As the estimated information, both family structure and floor space were selected. The classification result is evaluated using F-measure and accuracy. As a result, the accuracy of DFT-based classification was superior to the statistical method for detecting the floor space in a house.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-97
Number of pages6
ISBN (Electronic)9781509040759
DOIs
Publication statusPublished - 2016 Dec 8
Event7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 - Sydney, Australia
Duration: 2016 Nov 62016 Nov 9

Other

Other7th IEEE International Conference on Smart Grid Communications, SmartGridComm 2016
CountryAustralia
CitySydney
Period16/11/616/11/9

Keywords

  • classification
  • discrete fourier transform
  • feature extraction
  • power demand information
  • support vector machine

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Control and Optimization
  • Signal Processing

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  • Cite this

    Imanishi, T., Tennekoon, R., & Nishi, H. (2016). Feature extraction and classification using power demand information. In 2016 IEEE International Conference on Smart Grid Communications, SmartGridComm 2016 (pp. 92-97). [7778744] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SmartGridComm.2016.7778744