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Experimental and data collection methods for a large-scale smart grid deployment: Methods and first results

Author

Listed:
  • Rhodes, Joshua D.
  • Upshaw, Charles R.
  • Harris, Chioke B.
  • Meehan, Colin M.
  • Walling, David A.
  • Navrátil, Paul A.
  • Beck, Ariane L.
  • Nagasawa, Kazunori
  • Fares, Robert L.
  • Cole, Wesley J.
  • Kumar, Harsha
  • Duncan, Roger D.
  • Holcomb, Chris L.
  • Edgar, Thomas F.
  • Kwasinski, Alexis
  • Webber, Michael E.

Abstract

This paper has two objectives: 1) to describe the experimental and data collection methods for a large-scale smart grid deployment in Austin, Texas, and 2) to provide results based on those data. As of October 2012, the test bed was comprised of 1) 250 homes concentrated in a single neighborhood all built after 2007, and 2) 160 homes distributed throughout Austin with ages ranging from 10 to 92 years old. This experiment includes 200 electric monitoring systems (15-s resolution), 211 electric monitoring systems (1-min), 182 gas meters (2-cubic foot), and 51 water meters (1 gallon) and many of the monitored homes also have energy audits and homeowner surveys. The test bed also includes 185 rooftop PV (photovoltaic) installations and 50 electric vehicles in the same neighborhood. Data streams were automated and gathered at a supercomputing facility at UT-Austin yielding 250 GB (2.95 × 109 records) of data in the first year. This paper describes the baseline study and monitoring methods, characterizes the study participants, and provides some first results about residential energy use. These results include a negative correlation between energy use and knowledge about energy as well as a possible positive correlation between energy use and some rebates.

Suggested Citation

  • Rhodes, Joshua D. & Upshaw, Charles R. & Harris, Chioke B. & Meehan, Colin M. & Walling, David A. & Navrátil, Paul A. & Beck, Ariane L. & Nagasawa, Kazunori & Fares, Robert L. & Cole, Wesley J. & Kuma, 2014. "Experimental and data collection methods for a large-scale smart grid deployment: Methods and first results," Energy, Elsevier, vol. 65(C), pages 462-471.
    • Handle: RePEc:eee:energy:v:65:y:2014:i:c:p:462-471
    DOI: 10.1016/j.energy.2013.11.004
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    7. Rahman, Aowabin & Srikumar, Vivek & Smith, Amanda D., 2018. "Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 212(C), pages 372-385.
    8. Deetjen, Thomas A. & Vitter, J. Scott & Reimers, Andrew S. & Webber, Michael E., 2018. "Optimal dispatch and equipment sizing of a residential central utility plant for improving rooftop solar integration," Energy, Elsevier, vol. 147(C), pages 1044-1059.
    9. Tetsushi Ono & Aya Hagishima & Jun Tanimoto, 2022. "Non-Intrusive Detection of Occupants’ On/Off Behaviours of Residential Air Conditioning," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    10. Gouveia, João Pedro & Seixas, Júlia & Mestre, Ana, 2017. "Daily electricity consumption profiles from smart meters - Proxies of behavior for space heating and cooling," Energy, Elsevier, vol. 141(C), pages 108-122.
    11. Personal, Enrique & Guerrero, Juan Ignacio & Garcia, Antonio & Peña, Manuel & Leon, Carlos, 2014. "Key performance indicators: A useful tool to assess Smart Grid goals," Energy, Elsevier, vol. 76(C), pages 976-988.
    12. Barbour, Edward & Parra, David & Awwad, Zeyad & González, Marta C., 2018. "Community energy storage: A smart choice for the smart grid?," Applied Energy, Elsevier, vol. 212(C), pages 489-497.
    13. Job Taminiau & John P. Banks & Deborah Bleviss & John Byrne, 2019. "Advancing transformative sustainability: A comparative analysis of electricity service and supply innovators in the United States," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(4), July.
    14. Barbour, Edward & González, Marta C., 2018. "Projecting battery adoption in the prosumer era," Applied Energy, Elsevier, vol. 215(C), pages 356-370.
    15. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    16. Alonso-Abella, M. & Chenlo, F. & Nofuentes, G. & Torres-Ramírez, M., 2014. "Analysis of spectral effects on the energy yield of different PV (photovoltaic) technologies: The case of four specific sites," Energy, Elsevier, vol. 67(C), pages 435-443.
    17. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
    18. Anthony M Levenda, 2019. "Mobilizing smart grid experiments: Policy mobilities and urban energy governance," Environment and Planning C, , vol. 37(4), pages 634-651, June.

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