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Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring

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  • Mario E. Berges
  • Ethan Goldman
  • H. Scott Matthews
  • Lucio Soibelman

Abstract

Nonintrusive load monitoring (NILM) is a technique for deducing the power consumption and operational schedule of individual loads in a building from measurements of the overall voltage and current feeding it, using information and communication technologies. In this article, we review the potential of this technology to enhance residential electricity audits. First, we review the currently commercially available whole‐house and plug‐level technology for residential electricity monitoring in the context of supporting audits. We then contrast this with NILM and show the advantages and disadvantages of the approach by discussing results from a prototype system installed in an apartment unit. Recommendations for improving the technology to allow detailed, continuous appliance‐level auditing of residential buildings are provided, along with ideas for possible future work in the field.

Suggested Citation

  • Mario E. Berges & Ethan Goldman & H. Scott Matthews & Lucio Soibelman, 2010. "Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring," Journal of Industrial Ecology, Yale University, vol. 14(5), pages 844-858, October.
    • Handle: RePEc:bla:inecol:v:14:y:2010:i:5:p:844-858
    DOI: 10.1111/j.1530-9290.2010.00280.x
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    Cited by:

    1. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    2. Gustavo Felipe Martin Nascimento & Frédéric Wurtz & Patrick Kuo-Peng & Benoit Delinchant & Nelson Jhoe Batistela, 2022. "Quantifying Compressed Air Leakage through Non-Intrusive Load Monitoring Techniques in the Context of Energy Audits," Energies, MDPI, vol. 15(9), pages 1-24, April.
    3. Zhen Yang & Jinhong Du & Yiting Lin & Zhen Du & Li Xia & Qianchuan Zhao & Xiaohong Guan, 2022. "Increasing the energy efficiency of a data center based on machine learning," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 323-335, February.
    4. María Pérez-Ortiz & Silvia Jiménez-Fernández & Pedro A. Gutiérrez & Enrique Alexandre & César Hervás-Martínez & Sancho Salcedo-Sanz, 2016. "A Review of Classification Problems and Algorithms in Renewable Energy Applications," Energies, MDPI, vol. 9(8), pages 1-27, August.
    5. José Antonio Hoyo-Montaño & Guillermo Valencia-Palomo & Rafael Armando Galaz-Bustamante & Abel García-Barrientos & Daniel Fernando Espejel-Blanco, 2019. "Environmental Impacts of Energy Saving Actions in an Academic Building," Sustainability, MDPI, vol. 11(4), pages 1-20, February.
    6. Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
    7. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    8. Abubakar, I. & Khalid, S.N. & Mustafa, M.W. & Shareef, Hussain & Mustapha, M., 2017. "Application of load monitoring in appliances’ energy management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 235-245.
    9. Hao, He & Sanandaji, Borhan M. & Poolla, Kameshwar & Vincent, Tyrone L., 2015. "Potentials and economics of residential thermal loads providing regulation reserve," Energy Policy, Elsevier, vol. 79(C), pages 115-126.
    10. Aydinalp Koksal, Merih & Rowlands, Ian H. & Parker, Paul, 2015. "Energy, cost, and emission end-use profiles of homes: An Ontario (Canada) case study," Applied Energy, Elsevier, vol. 142(C), pages 303-316.

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