Feature extraction and background information detection method using power demand

Masahiro Yoshida, Tomoya Imanishi, Hiroaki Nishi

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

3 Citations (Scopus)


Recently, many electricity retailers have been aggregating consumer power demand information from smart meter infrastructure, and applications that utilize these data are widely studied. For example, the estimation of customers' background information using their power demand information has attracted much interest; this information can be utilized in the marketing field for background-targeted advertising. In order to utilize power demand information effectively, an appropriate feature extraction method must be applied. In this paper, appropriate data extraction methods specific to power demand information are proposed. In the experiment, power demand data for Kawasaki city were used, and 19 feature data were extracted using the proposed method. The utility of the extracted features was assessed through the performance of classification estimation for two background information types, family structure and floor space. The classification problems are solved by applying two typical machine-learning algorithms, the support vector machine and k-nearest neighbor. In particular, analysis of variance (ANOVA) was applied to the 19 feature data, which were ranked according to the F value. Then, the n (n = [1, 2, 19]) best feature data were used as the input step by step, and the score for each condition was computed to derive the best feature set. According to the results, some of the feature data were considered to be irrelevant, and the best feature data set was successfully selected. Furthermore, thee scores when raw data were input were also computed and compared with the scores when the best feature data set was used. As a result, the performance was better when using processed data instead of raw data.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Symposium on Industrial Electronics, ISIE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509014125
Publication statusPublished - 2017 Aug 3
Event26th IEEE International Symposium on Industrial Electronics, ISIE 2017 - Edinburgh, Scotland, United Kingdom
Duration: 2017 Jun 182017 Jun 21

Publication series

NameIEEE International Symposium on Industrial Electronics


Other26th IEEE International Symposium on Industrial Electronics, ISIE 2017
Country/TerritoryUnited Kingdom
CityEdinburgh, Scotland


  • Feature extraction
  • K nearest neighbor
  • Power demand
  • Smart meter
  • Support vector machine

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering


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