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Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters

Author

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  • Bishnu Nepal

    (Department of Architecture, Chubu University, 487-8501 Kasugai, Japan)

  • Motoi Yamaha

    (Department of Architecture, Chubu University, 487-8501 Kasugai, Japan)

  • Hiroya Sahashi

    (Department of Architecture, Chubu University, 487-8501 Kasugai, Japan)

  • Aya Yokoe

    (Department of Architecture, Chubu University, 487-8501 Kasugai, Japan)

Abstract

Energy demands in the building sector account for more than 30% of the total energy use and more than 55% of the global electricity demand. Efforts to develop sustainable buildings are progressing but are still not keeping pace with the growing building sector and the rising demand for energy. Analyzing the energy use pattern of buildings and planning for energy conservation in existing buildings are essential. In this research, we propose a method to analyze the energy use pattern in a building using the K-means clustering method. Initial centroids in K-means clustering are chosen randomly so that the clustering result changes every time. This instability is removed in the proposed method by the selection of initial centroids using a percentile method based on empirical cumulative distribution. The results from the proposed method have better accuracy, and the internal cohesion and separation between clusters are better than the random initialization method. Analyzing yearly electricity use using the proposed clustering method, the daily pattern of electricity use can be categorized according to the operation of buildings. For this purpose, in this research, electricity use pattern was analyzed for three to six clusters. In comparison with the university schedule, six clusters were found to be appropriate and the accuracy was 89.3%. Once daily electricity use are categorized, base electricity consumption, electricity consumption by human activities, and electricity consumption by air-conditioning can be determined. As energy consumption by usage is clarified, measures for energy consumption in university buildings can be proposed.

Suggested Citation

  • Bishnu Nepal & Motoi Yamaha & Hiroya Sahashi & Aya Yokoe, 2019. "Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters," Energies, MDPI, vol. 12(12), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2451-:d:242911
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    References listed on IDEAS

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    1. Félix Iglesias & Wolfgang Kastner, 2013. "Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns," Energies, MDPI, vol. 6(2), pages 1-19, January.
    2. Yu, Zhun (Jerry) & Haghighat, Fariborz & Fung, Benjamin C.M. & Morofsky, Edward & Yoshino, Hiroshi, 2011. "A methodology for identifying and improving occupant behavior in residential buildings," Energy, Elsevier, vol. 36(11), pages 6596-6608.
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    Cited by:

    1. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    2. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
    3. Krzysztof Przystupa & Julia Pyrih & Mykola Beshley & Mykhailo Klymash & Andriy Branytskyy & Halyna Beshley & Daniel Pieniak & Konrad Gauda, 2021. "Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing," Energies, MDPI, vol. 14(8), pages 1-24, April.
    4. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.

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