Classifying Appliance Ownership Status using Residential CO<sub>2</sub> Emission Survey Data

DOI
  • Mukai Toshihiro
    一般財団法人 電力中央研究所　社会経済研究所
  • Tanaka Takuro
    一般財団法人 電力中央研究所　社会経済研究所

Bibliographic Information

Other Title
  • 家庭CO<sub>2</sub>統計を用いた機器保有状況の分類手法に関する検証

Abstract

Attention has been focused on efforts to promote energy saving actions by utilizing various data regarding energy use of households. When promoting energy-saving behavior, analysis and classification of households’ equipment ownership status will be able to be used for the personalization of energy-saving tips such as energy-saving measures and home appliance replacement in order to promote behavior change effectively. In this paper, we compared the accuracy of methods for classifying equipment ownership status using the national survey of carbon dioxide emissions from residential sector. We employed four classification methods for comparison: binary logistic regression, decision tree, random forest and XGBoosting. We also evaluated the importance of explanatory variables by using permutation test, which remove variables’ feature by randomizing the values of each variable. We find that machine learning methods such as Random Forest and XGBoosting generate relatively higher classification accuracy. Furthermore, these methods show less degradation of classification accuracy when important explanatory variables are permuted from the full model.

Journal

Keywords

Details 詳細情報について

  • CRID
    1391412326429379712
  • NII Article ID
    130007938039
  • DOI
    10.24778/jjser.41.6_328
  • ISSN
    24330531
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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