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A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Author

Listed:
  • Fateh Nassim Melzi

    (IRT SystemX, 8 Avenue de la Vauve, 91120 Palaiseau, Paris Saclay, France)

  • Allou Same

    (IFSTTAR, 14-20 Boulevard Newton, 77420 Champs-sur-Marne, France)

  • Mohamed Haykel Zayani

    (IRT SystemX, 8 Avenue de la Vauve, 91120 Palaiseau, Paris Saclay, France)

  • Latifa Oukhellou

    (IFSTTAR, 14-20 Boulevard Newton, 77420 Champs-sur-Marne, France)

Abstract

The large amount of data collected by smart meters is a valuable resource that can be used to better understand consumer behavior and optimize electricity consumption in cities. This paper presents an unsupervised classification approach for extracting typical consumption patterns from data generated by smart electric meters. The proposed approach is based on a constrained Gaussian mixture model whose parameters vary according to the day type (weekday, Saturday or Sunday). The proposed methodology is applied to a real dataset of Irish households collected by smart meters over one year. For each cluster, the model provides three consumption profiles that depend on the day type. In the first instance, the model is applied on the electricity consumption of users during one month to extract groups of consumers who exhibit similar consumption behaviors. The clustering results are then crossed with contextual variables available for the households to show the close links between electricity consumption and household socio-economic characteristics. At the second instance, the evolution of the consumer behavior from one month to another is assessed through variations of cluster sizes over time. The results show that the consumer behavior evolves over time depending on the contextual variables such as temperature fluctuations and calendar events.

Suggested Citation

  • Fateh Nassim Melzi & Allou Same & Mohamed Haykel Zayani & Latifa Oukhellou, 2017. "A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors," Energies, MDPI, vol. 10(10), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1446-:d:112719
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    References listed on IDEAS

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    1. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    2. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
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    Cited by:

    1. Jovani Taveira de Souza & Antonio Carlos de Francisco & Cassiano Moro Piekarski & Guilherme Francisco do Prado, 2019. "Data Mining and Machine Learning to Promote Smart Cities: A Systematic Review from 2000 to 2018," Sustainability, MDPI, vol. 11(4), pages 1-14, February.
    2. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    3. van Zoest, Vera & El Gohary, Fouad & Ngai, Edith C.H. & Bartusch, Cajsa, 2021. "Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector," Applied Energy, Elsevier, vol. 302(C).
    4. Yeong Huei Lee & Mugahed Amran & Yee Yong Lee & Ahmad Beng Hong Kueh & Siaw Fui Kiew & Roman Fediuk & Nikolai Vatin & Yuriy Vasilev, 2021. "Thermal Behavior and Energy Efficiency of Modified Concretes in the Tropical Climate: A Systemic Review," Sustainability, MDPI, vol. 13(21), pages 1, October.
    5. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    6. Nikoleta Andreadou & Evangelos Kotsakis & Marcelo Masera, 2018. "Smart Meter Traffic in a Real LV Distribution Network," Energies, MDPI, vol. 11(5), pages 1-27, May.
    7. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.
    8. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).

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