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Enhancing energy efficiency in the residential sector with smart meter data analytics

Author

Listed:
  • Konstantin Hopf

    (University of Bamberg)

  • Mariya Sodenkamp

    (University of Bamberg)

  • Thorsten Staake

    (University of Bamberg
    ETH Zürich)

Abstract

Tailored energy efficiency campaigns that make use of household-specific information can trigger substantial energy savings in the residential sector. The information required for such campaigns, however, is often missing. We show that utility companies can extract that information from smart meter data using machine learning. We derive 133 features from smart meter and weather data and use the Random Forest classifier that allows us to recognize 19 household classes related to 11 household characteristics (e.g., electric heating, size of dwelling) with an accuracy of up to 95% (69% on average). The results indicate that even datasets with an hourly or daily resolution are sufficient to impute key household characteristics with decent accuracy and that data from different yearly seasons does not considerably influence the classification performance. Furthermore, we demonstrate that a small training data set consisting of only 200 households already reaches a good performance. Our work may serve as benchmark for upcoming, similar research on smart meter data and provide guidance for practitioners for estimating the efforts of implementing such analytics solutions.

Suggested Citation

  • Konstantin Hopf & Mariya Sodenkamp & Thorsten Staake, 2018. "Enhancing energy efficiency in the residential sector with smart meter data analytics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 453-473, November.
    • Handle: RePEc:spr:elmark:v:28:y:2018:i:4:d:10.1007_s12525-018-0290-9
    DOI: 10.1007/s12525-018-0290-9
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Al Khafaf, Nameer & Rezaei, Ahmad Asgharian & Moradi Amani, Ali & Jalili, Mahdi & McGrath, Brendan & Meegahapola, Lasantha & Vahidnia, Arash, 2022. "Impact of battery storage on residential energy consumption: An Australian case study based on smart meter data," Renewable Energy, Elsevier, vol. 182(C), pages 390-400.
    2. Weigert, Andreas & Hopf, Konstantin & Günther, Sebastian A. & Staake, Thorsten, 2022. "Heat pump inspections result in large energy savings when a pre-selection of households is performed: A promising use case of smart meter data," Energy Policy, Elsevier, vol. 169(C).
    3. Barbara Dinter & Jan Krämer, 2018. "Data-driven innovations in electronic markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 403-405, November.
    4. Rainer Alt, 2020. "Electronic Markets on sustainability," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 667-674, December.

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    More about this item

    Keywords

    Green information systems; Decision support systems; Data analytics; Energy efficiency; Sustainability; Classification;
    All these keywords.

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • D10 - Microeconomics - - Household Behavior - - - General
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • Q20 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - General
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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