IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v159y2018icp302-309.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218311988
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.06.133?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mirzakhani, M. Amin & Tahouni, Nassim & Panjeshahi, M. Hassan, 2017. "Energy benchmarking of cement industry, based on Process Integration concepts," Energy, Elsevier, vol. 130(C), pages 382-391.
    2. Gilbert, Alexander Q. & Sovacool, Benjamin K., 2017. "Benchmarking natural gas and coal-fired electricity generation in the United States," Energy, Elsevier, vol. 134(C), pages 622-628.
    3. Iribarren, Diego & Vázquez-Rowe, Ian & Rugani, Benedetto & Benetto, Enrico, 2014. "On the feasibility of using emergy analysis as a source of benchmarking criteria through data envelopment analysis: A case study for wind energy," Energy, Elsevier, vol. 67(C), pages 527-537.
    4. Meng, Ting & Hsu, David & Han, Albert, 2017. "Estimating energy savings from benchmarking policies in New York City," Energy, Elsevier, vol. 133(C), pages 415-423.
    5. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    6. Lee, Wen-Shing, 2010. "Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes," Energy, Elsevier, vol. 35(1), pages 50-54.
    7. Lee, Wen-Shing & Lin, Yeong-Chuan, 2011. "Evaluating and ranking energy performance of office buildings using Grey relational analysis," Energy, Elsevier, vol. 36(5), pages 2551-2556.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. Lee, Dasom & Hess, David J., 2021. "Data privacy and residential smart meters: Comparative analysis and harmonization potential," Utilities Policy, Elsevier, vol. 70(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Jiaxin & Wang, Zihan & Cheng, Xin & Shuai, Jing & Shuai, Chuanmin & Liu, Jing, 2020. "Has solar PV achieved the national poverty alleviation goals? Empirical evidence from the performances of 52 villages in rural China," Energy, Elsevier, vol. 201(C).
    2. Wang, H., 2015. "A generalized MCDA–DEA (multi-criterion decision analysis–data envelopment analysis) approach to construct slacks-based composite indicator," Energy, Elsevier, vol. 80(C), pages 114-122.
    3. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Improvements of the operational rating system for existing residential buildings," Applied Energy, Elsevier, vol. 193(C), pages 112-124.
    4. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    5. Wang, Zhiwei & Lei, Tingzhou & Chang, Xia & Shi, Xinguang & Xiao, Ju & Li, Zaifeng & He, Xiaofeng & Zhu, Jinling & Yang, Shuhua, 2015. "Optimization of a biomass briquette fuel system based on grey relational analysis and analytic hierarchy process: A study using cornstalks in China," Applied Energy, Elsevier, vol. 157(C), pages 523-532.
    6. Fathi Alarabi Yosef & Luay Jum’a & Muntasir Alatoom, 2023. "Identifying and Categorizing Sustainable Supply Chain Practices Based on Triple Bottom Line Dimensions: Evaluation of Practice Implementation in the Cement Industry," Sustainability, MDPI, vol. 15(9), pages 1-22, April.
    7. Ibrahim Yilmaz, 2023. "A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    8. Luming Shang & Sofia Dermisi & Youngjun Choe & Hyun Woo Lee & Yohan Min, 2023. "Assessing Office Building Marketability before and after the Implementation of Energy Benchmarking and Disclosure Policies—Lessons Learned from Major U.S. Cities," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    9. Lee, Wen-Shing & Kung, Chung-Kuan, 2011. "Using climate classification to evaluate building energy performance," Energy, Elsevier, vol. 36(3), pages 1797-1801.
    10. Liang, Yingzong & Hui, Chi Wai, 2018. "Convexification for natural gas transmission networks optimization," Energy, Elsevier, vol. 158(C), pages 1001-1016.
    11. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    12. Xiongfeng Pan & Jing Zhang & Changyu Li & Xianyou Pan & Jinbo Song, 2019. "Analysis of China’s regional wind power generation efficiency and its influencing factors," Energy & Environment, , vol. 30(2), pages 254-271, March.
    13. Freida Ozavize Ayodele & Siti Indati Mustapa & Bamidele Victor Ayodele & Norsyahida Mohammad, 2020. "An Overview of Economic Analysis and Environmental Impacts of Natural Gas Conversion Technologies," Sustainability, MDPI, vol. 12(23), pages 1-18, December.
    14. Lai, Yuan & Papadopoulos, Sokratis & Fuerst, Franz & Pivo, Gary & Sagi, Jacob & Kontokosta, Constantine E., 2022. "Building retrofit hurdle rates and risk aversion in energy efficiency investments," Applied Energy, Elsevier, vol. 306(PB).
    15. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    16. Naseri, Hakim & Parashkoohi, Mohammad Gholami & Ranjbar, Iraj & Zamani, Davood Mohammad, 2021. "Energy-economic and life cycle assessment of sugarcane production in different tillage systems," Energy, Elsevier, vol. 217(C).
    17. Apergis, Nicholas & Aye, Goodness C. & Barros, Carlos Pestana & Gupta, Rangan & Wanke, Peter, 2015. "Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs," Energy Economics, Elsevier, vol. 51(C), pages 45-53.
    18. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    19. Jia, Hongxiang & Li, Tianjiao & Wang, Anjian & Liu, Guwang & Guo, Xiaoqian, 2021. "Decoupling analysis of economic growth and mineral resources consumption in China from 1992 to 2017: A comparison between tonnage and exergy perspective," Resources Policy, Elsevier, vol. 74(C).
    20. Alev Taskin Gumus & A. Yesim Yayla & Erkan Çelik & Aytac Yildiz, 2013. "A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey," Energies, MDPI, vol. 6(6), pages 1-16, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:159:y:2018:i:c:p:302-309. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.