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Determining appliance energy usage with a high-resolution metering system for residential natural gas meters

Author

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  • Tewolde, M.
  • Longtin, J.P.
  • Das, S.R.
  • Sharma, S.

Abstract

This paper presents a high-resolution automated meter reading system for residential gas meters, which can be used to record gas consumption for each appliance. The mechanical operation of an industry-standard residential gas meter is characterized, and the internal metering mechanism analyzed to develop a system to non-intrusively monitor gas consumption of individual appliances by resolving small amounts of gas usage at the meter. The system can be retrofitted to an existing gas meter with a module that includes a high-resolution encoder to collect gas flow data, and a microprocessor to analyze and classify appliance load profiles. This approach provides a number of attractive features including low cost, easy installation and integration with existing meter reading technologies. This system enables gas utilities to provide real-time feedback to customers on gas usage by appliance.

Suggested Citation

  • Tewolde, M. & Longtin, J.P. & Das, S.R. & Sharma, S., 2013. "Determining appliance energy usage with a high-resolution metering system for residential natural gas meters," Applied Energy, Elsevier, vol. 108(C), pages 363-372.
    • Handle: RePEc:eee:appene:v:108:y:2013:i:c:p:363-372
    DOI: 10.1016/j.apenergy.2013.03.032
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    References listed on IDEAS

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    1. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
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    Cited by:

    1. Chou, Jui-Sheng & Gusti Ayu Novi Yutami, I, 2014. "Smart meter adoption and deployment strategy for residential buildings in Indonesia," Applied Energy, Elsevier, vol. 128(C), pages 336-349.
    2. Chévez, Pedro Joaquín & Martini, Irene & Discoli, Carlos, 2019. "Methodology developed for the construction of an urban-energy diagnosis aimed to assess alternative scenarios: An intra-urban approach to foster cities’ sustainability," Applied Energy, Elsevier, vol. 237(C), pages 751-778.
    3. Cominola, A. & Giuliani, M. & Piga, D. & Castelletti, A. & Rizzoli, A.E., 2017. "A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring," Applied Energy, Elsevier, vol. 185(P1), pages 331-344.
    4. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.

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