Extraction of basic patterns of household energy consumption

Haoyang Shen; Hideitsu Hino; Noboru Murata; Shinji Wakao

doi:10.1109/ICMLA.2011.68

Extraction of basic patterns of household energy consumption

Haoyang Shen^*, Hideitsu Hino, Noboru Murata, Shinji Wakao

^*Corresponding author for this work

School of Advanced Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Pages	275-280
Number of pages	6
DOIs	https://doi.org/10.1109/ICMLA.2011.68
Publication status	Published - 2011
Event	10th International Conference on Machine Learning and Applications, ICMLA 2011 - Honolulu, HI, United States Duration: 2011 Dec 18 → 2011 Dec 21

Publication series

Name	Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume	2

Conference

Conference	10th International Conference on Machine Learning and Applications, ICMLA 2011
Country/Territory	United States
City	Honolulu, HI
Period	11/12/18 → 11/12/21

Keywords

Gaussian mixture model
KL-divergence
energy consumption pattern
hierarchical clustering

ASJC Scopus subject areas

Computer Science Applications
Human-Computer Interaction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICMLA.2011.68

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). Article 6147687 (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2). https://doi.org/10.1109/ICMLA.2011.68

Extraction of basic patterns of household energy consumption. / Shen, Haoyang; Hino, Hideitsu; Murata, Noboru et al.
Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011. 2011. p. 275-280 6147687 (Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 6147687, Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011, vol. 2, pp. 275-280, 10th International Conference on Machine Learning and Applications, ICMLA 2011, Honolulu, HI, United States, 11/12/18. https://doi.org/10.1109/ICMLA.2011.68

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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.",

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AU - Hino, Hideitsu

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AB - 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.

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Extraction of basic patterns of household energy consumption

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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