Extraction of basic patterns of household energy consumption

Haoyang Shen, Hideitsu Hino, Noboru Murata, Shinji Wakao

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

4 Citations (Scopus)

Abstract

Solar power, wind power, and co-generation (combined heat and power) systems are possible candidate for household power generation. These systems have their advantages and disadvantages. To propose the optimal combination of the power generation systems, the extraction of basic patterns of energy consumption of the house is required. In this study, energy consumption patterns are modeled by mixtures of Gaussian distributions. Then, using the symmetrized Kullback-Leibler divergence as a distance measure of the distributions, the basic pattern of energy consumption is extracted by means of hierarchical clustering. By an experiment using the Annex 42 dataset, it is shown that the proposed method is able to extract typical energy consumption patterns.

Original languageEnglish
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages275-280
Number of pages6
DOIs
Publication statusPublished - 2011 Dec 1
Event10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States
Duration: 2011 Dec 182011 Dec 21

Publication series

NameProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume2

Conference

Conference10th International Conference on Machine Learning and Applications, ICMLA 2011
CountryUnited States
CityHonolulu, HI
Period11/12/1811/12/21

Keywords

  • Gaussian mixture model
  • KL-divergence
  • energy consumption pattern
  • hierarchical clustering

ASJC Scopus subject areas

  • Computer Science Applications
  • Human-Computer Interaction

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  • Cite this

    Shen, H., Hino, H., Murata, N., & Wakao, S. (2011). Extraction of basic patterns of household energy consumption. In Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (pp. 275-280). [6147687] (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2). https://doi.org/10.1109/ICMLA.2011.68