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A data-driven approach to identify households with plug-in electrical vehicles (PEVs)

Author

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  • Verma, Anoop
  • Asadi, Ali
  • Yang, Kai
  • Tyagi, Satish

Abstract

In recent years popularity of plug-in electric (PEV) vehicles has grown significantly. Charging of such vehicles is typically done at home from a standard outlet or at corporate car locations and thus adds extra load on the distribution grid. Due to high power consumption of PEV charging, the utility industries face enormous challenges to provide this extra demand. The identification of charging patterns of PEV is thus of paramount importance to balance the electric load and assure coordinated charging. More specifically, there is a need to identify users with PEVs to better manage the load distribution. In the present research, an analysis based on energy envelopes of the usage patterns is performed. A set of well-known data mining algorithms are used to identify the best classifier to help identify customers with PEVs.

Suggested Citation

  • Verma, Anoop & Asadi, Ali & Yang, Kai & Tyagi, Satish, 2015. "A data-driven approach to identify households with plug-in electrical vehicles (PEVs)," Applied Energy, Elsevier, vol. 160(C), pages 71-79.
    • Handle: RePEc:eee:appene:v:160:y:2015:i:c:p:71-79
    DOI: 10.1016/j.apenergy.2015.09.013
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    References listed on IDEAS

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    7. Khemakhem, Siwar & Rekik, Mouna & Krichen, Lotfi, 2019. "Double layer home energy supervision strategies based on demand response and plug-in electric vehicle control for flattening power load curves in a smart grid," Energy, Elsevier, vol. 167(C), pages 312-324.
    8. Milan Straka & Pasquale De Falco & Gabriella Ferruzzi & Daniela Proto & Gijs van der Poel & Shahab Khormali & v{L}ubov{s} Buzna, 2019. "Predicting popularity of EV charging infrastructure from GIS data," Papers 1910.02498, arXiv.org.
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