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An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings

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  • Livingston, Olga V.
  • Pulsipher, Trenton C.
  • Anderson, David M.
  • Vlachokostas, Alex
  • Wang, Na

Abstract

A growing number of cities are adopting energy use benchmarking ordinances, which require building owners report their buildings' total energy usage annually. It also requires that utilities supply aggregated building-level monthly energy consumption data. The data aggregation poses privacy concerns, as it is possible to estimate the individual tenant load consumption curve by dividing aggregated energy data by the number of meters. A solution is to quantify and assess the impact of adjusting the utility meter aggregation threshold on tenant privacy and on buildings that are eligible for energy usage reporting. As the threshold increases, fewer buildings are eligible for energy use data disclosure and therefore lessening data value. This study aims to investigate the similarity between individual utility meters and whole-building totals at various aggregation levels. Based on statistical analysis of 715,000 anonymized, non-residential meter accounts from six utilities across the U.S., we developed the “Meter Aggregation Selection Threshold” as a metric to assess tenant privacy risk. The metric estimates the portion of individual customer energy use patterns that are similar to the aggregated building consumption profile. It allows policy makers to make an informed decision on whether required disclosure regulations compromise business sensitive information and tenant privacy.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:302-309
    DOI: 10.1016/j.energy.2018.06.133
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    References listed on IDEAS

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    Cited by:

    1. 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).
    2. Sungkyun Ha & Sungho Tae & Rakhyun Kim, 2019. "Energy Demand Forecast Models for Commercial Buildings in South Korea," Energies, MDPI, vol. 12(12), pages 1-19, June.
    3. Amir Mosavi & Mohsen Salimi & Sina Faizollahzadeh Ardabili & Timon Rabczuk & Shahaboddin Shamshirband & Annamaria R. Varkonyi-Koczy, 2019. "State of the Art of Machine Learning Models in Energy Systems, a Systematic Review," Energies, MDPI, vol. 12(7), pages 1-42, April.
    4. Lee, Dasom & Hess, David J., 2021. "Data privacy and residential smart meters: Comparative analysis and harmonization potential," Utilities Policy, Elsevier, vol. 70(C).

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