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Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

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  • Muhammad Fahim

    (Institute of Information Systems, Innopolis University, Innopolis 420500, Republic of Tatarstan, Russia)

  • Alberto Sillitti

    (Institute of Information Systems, Innopolis University, Innopolis 420500, Republic of Tatarstan, Russia)

Abstract

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

Suggested Citation

  • Muhammad Fahim & Alberto Sillitti, 2019. "Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters," Energies, MDPI, vol. 12(5), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:773-:d:209083
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    References listed on IDEAS

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    1. Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
    2. Peng Du & Antony Wood & Brent Stephens, 2016. "Empirical Operational Energy Analysis of Downtown High-Rise vs. Suburban Low-Rise Lifestyles: A Chicago Case Study," Energies, MDPI, vol. 9(6), pages 1-27, June.
    3. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2013. "Evaluation of time series techniques to characterise domestic electricity demand," Energy, Elsevier, vol. 50(C), pages 120-130.
    4. du Preez, Johann & Witt, Stephen F., 2003. "Univariate versus multivariate time series forecasting: an application to international tourism demand," International Journal of Forecasting, Elsevier, vol. 19(3), pages 435-451.
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    Cited by:

    1. Xiao-Yu Zhang & Stefanie Kuenzel & José-Rodrigo Córdoba-Pachón & Chris Watkins, 2020. "Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System," Energies, MDPI, vol. 13(12), pages 1-30, June.
    2. Corina Pelau & Carmen Acatrinei, 2019. "The Paradox of Energy Consumption Decrease in the Transition Period towards a Digital Society," Energies, MDPI, vol. 12(8), pages 1-16, April.

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