Feature Extraction and Resident Number Prediction Method using Power Consumption Data

• Yoshida Masahiro

  Hiroaki Nishi Laboratory, Graduate School of Science for Opens and Environmental Systems, Keio University

• Imanishi Tomoya

  Hiroaki Nishi Laboratory, Graduate School of Science for Opens and Environmental Systems, Keio University

• Nishi Hiroaki

  Department of System Design Engineering, Faculty of Science and Technology, Keio University

Abstract

A large amount of power consumption information generated from private houses is being aggregated nowadays, particularly by the spread of smart electric meters. Applications that utilize these data are widely studied, and several services have been proposed. In order to utilize the power demand information effectively, an appropriate feature extraction and selection method is necessary. In this paper, a feature extraction method for power consumption information is proposed. Extracted features are used to predict the “number of household members (number of residents)” using typical machine learning algorithms, namely, Support Vector Machine (SVM), k-Nearest Neighborhood (k-NN), and Random Forest (RF). The number of residents represents significant information for the marketing departments of several industries such as real estate and construction industries. The proposed feature extraction method consists of two steps: feature variable generation and feature variable selection. In the feature variable generation step, we have used both fundamental statics and an ARMA model to generate 33 feature variables. In the feature variable selection step, the extracted feature variables are first ranked by applying Analysis of Variance (ANOVA). An appropriate feature variable set is selected by assessing several combinations of the 33 features, based on proposed extended algorithm of Recursive Feature Elimination (RFE). Our overall feature extraction method is evaluated based on the prediction accuracy using extracted feature variables. Compared with the accuracy using feature variables extracted by conventional methods, the accuracy is improved by 6.78%, 4.98%, and 8.11% for k-NN, SVM, and RF, respectively, and we have successfully proven validity of our proposition.

Journal

• IEEJ Transactions on Electronics, Information and Systems

  IEEJ Transactions on Electronics, Information and Systems 139 (3), 227-236, 2019-03-01

  The Institute of Electrical Engineers of Japan

Keywords

• smart electric meter
• feature extraction
• feature selection
• analysis of variance (ANOVA)
• ARMA model
• machine learning

Details

• CRID: 1390564238078909568
• NII Article ID: 130007607255
• NII Book ID: AN10065950
• DOI: 10.1541/ieejeiss.139.227
• ISSN: 13488155
• ISSN: 03854221
• NDL BIB ID: 029552750
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I029552750
• Web Site: https://www.jstage.jst.go.jp/article/ieejeiss/139/3/139_227/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed