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Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data

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  • Krzysztof Gajowniczek

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland)

  • Tomasz Ząbkowski

    (Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland)

Abstract

The main goal of this research is to discover the structure of home appliances usage patterns, hence providing more intelligence in smart metering systems by taking into account the usage of selected home appliances and the time of their usage. In particular, we present and apply a set of unsupervised machine learning techniques to reveal specific usage patterns observed at an individual household. The work delivers the solutions applicable in smart metering systems that might: (1) contribute to higher energy awareness; (2) support accurate usage forecasting; and (3) provide the input for demand response systems in homes with timely energy saving recommendations for users. The results provided in this paper show that determining household characteristics from smart meter data is feasible and allows for quickly grasping general trends in data.

Suggested Citation

  • Krzysztof Gajowniczek & Tomasz Ząbkowski, 2015. "Data Mining Techniques for Detecting Household Characteristics Based on Smart Meter Data," Energies, MDPI, vol. 8(7), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:7:p:7407-7427:d:52958
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    References listed on IDEAS

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    Cited by:

    1. Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
    2. Qadrdan, Meysam & Fazeli, Reza & Jenkins, Nick & Strbac, Goran & Sansom, Robert, 2019. "Gas and electricity supply implications of decarbonising heat sector in GB," Energy, Elsevier, vol. 169(C), pages 50-60.
    3. Yazhou Jiang & Chen-Ching Liu & Yin Xu, 2016. "Smart Distribution Systems," Energies, MDPI, vol. 9(4), pages 1-20, April.
    4. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    5. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    6. Ahir, Rajesh K. & Chakraborty, Basab, 2021. "A meta-analytic approach for determining the success factors for energy conservation," Energy, Elsevier, vol. 230(C).
    7. Matteo Caldera & Asad Hussain & Sabrina Romano & Valerio Re, 2023. "Energy-Consumption Pattern-Detecting Technique for Household Appliances for Smart Home Platform," Energies, MDPI, vol. 16(2), pages 1-23, January.
    8. Yang, Wangwang & Shi, Jing & Li, Shujian & Song, Zhaofang & Zhang, Zitong & Chen, Zexu, 2022. "A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior," Applied Energy, Elsevier, vol. 307(C).
    9. Fernando Ulloa-Vásquez & Victor Heredia-Figueroa & Cristóbal Espinoza-Iriarte & José Tobar-Ríos & Fernanda Aguayo-Reyes & Dante Carrizo & Luis García-Santander, 2024. "Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning," Energies, MDPI, vol. 17(6), pages 1-19, March.
    10. Shailendra Singh & Abdulsalam Yassine, 2018. "Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting," Energies, MDPI, vol. 11(2), pages 1-26, February.

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