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Data privacy and residential smart meters: Comparative analysis and harmonization potential

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  • Lee, Dasom
  • Hess, David J.

Abstract

Building on privacy principles of the Fair Information Practice Principles and the European Union's General Data Protection Regulation, the study compares national policies and programs in Europe and North America and identifies prevailing practices for implementing privacy goals for residential energy customers: customer opt-out policies, sampling and sharing guidelines, independent data storage, and governmental enforcement authority. The analysis provides the basis for privacy standards that could apply to advanced-metering customer data across countries, even with rapidly evolving technology.

Suggested Citation

  • Lee, Dasom & Hess, David J., 2021. "Data privacy and residential smart meters: Comparative analysis and harmonization potential," Utilities Policy, Elsevier, vol. 70(C).
  • Handle: RePEc:eee:juipol:v:70:y:2021:i:c:s0957178721000229
    DOI: 10.1016/j.jup.2021.101188
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    References listed on IDEAS

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    1. Livingston, Olga V. & Pulsipher, Trenton C. & Anderson, David M. & Vlachokostas, Alex & Wang, Na, 2018. "An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings," Energy, Elsevier, vol. 159(C), pages 302-309.
    2. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    3. Sovacool, Benjamin K. & Kivimaa, Paula & Hielscher, Sabine & Jenkins, Kirsten, 2017. "Vulnerability and resistance in the United Kingdom's smart meter transition," Energy Policy, Elsevier, vol. 109(C), pages 767-781.
    4. Ruddell, Benjamin L. & Cheng, Dan & Fournier, Eric Daniel & Pincetl, Stephanie & Potter, Caryn & Rushforth, Richard, 2020. "Guidance on the usability-privacy tradeoff for utility customer data aggregation," Utilities Policy, Elsevier, vol. 67(C).
    5. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    6. Michalec, Aleksandra & Hayes, Enda & Longhurst, James & Tudgey, David, 2019. "Enhancing the communication potential of smart metering for energy and water," Utilities Policy, Elsevier, vol. 56(C), pages 33-40.
    7. Buchmann, Marius, 2017. "Governance of data and information management in smart distribution grids: Increase efficiency by balancing coordination and competition," Utilities Policy, Elsevier, vol. 44(C), pages 63-72.
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    Citations

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    Cited by:

    1. Marcel Antal & Vlad Mihailescu & Tudor Cioara & Ionut Anghel, 2022. "Blockchain-Based Distributed Federated Learning in Smart Grid," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    2. Lee, Dasom & Hess, David J. & Heldeweg, Michiel A., 2022. "Safety and privacy regulations for unmanned aerial vehicles: A multiple comparative analysis," Technology in Society, Elsevier, vol. 71(C).
    3. Dasom Lee & David J. Hess, 2022. "Public concerns and connected and automated vehicles: safety, privacy, and data security," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-13, December.
    4. Mayurkumar Rajkumar Balwani & Karthik Thirumala & Vivek Mohan & Siqi Bu & Mini Shaji Thomas, 2021. "Development of a Smart Meter for Power Quality-Based Tariff Implementation in a Smart Grid," Energies, MDPI, vol. 14(19), pages 1-21, September.
    5. Reif, Valerie & Meeus, Leonardo, 2022. "Smart metering interoperability issues and solutions: Taking inspiration from other ecosystems and sectors," Utilities Policy, Elsevier, vol. 76(C).

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