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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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    1. Doucette, Reed T. & McCulloch, Malcolm D., 2011. "Modeling the prospects of plug-in hybrid electric vehicles to reduce CO2 emissions," Applied Energy, Elsevier, vol. 88(7), pages 2315-2323, July.
    2. Farzan, Farbod & Jafari, Mohsen A. & Gong, Jie & Farzan, Farnaz & Stryker, Andrew, 2015. "A multi-scale adaptive model of residential energy demand," Applied Energy, Elsevier, vol. 150(C), pages 258-273.
    3. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    4. Kelly, Jarod C. & MacDonald, Jason S. & Keoleian, Gregory A., 2012. "Time-dependent plug-in hybrid electric vehicle charging based on national driving patterns and demographics," Applied Energy, Elsevier, vol. 94(C), pages 395-405.
    5. Munkhammar, Joakim & Widén, Joakim & Rydén, Jesper, 2015. "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, Elsevier, vol. 142(C), pages 135-143.
    6. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    7. Wei, Xiupeng & Xu, Guanglin & Kusiak, Andrew, 2014. "Modeling and optimization of a chiller plant," Energy, Elsevier, vol. 73(C), pages 898-907.
    8. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    9. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    10. Wu, Xiaolan & Cao, Binggang & Li, Xueyan & Xu, Jun & Ren, Xiaolong, 2011. "Component sizing optimization of plug-in hybrid electric vehicles," Applied Energy, Elsevier, vol. 88(3), pages 799-804, March.
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    Cited by:

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    2. Yang Yu & Guangyi Liu & Wendong Zhu & Fei Wang & Bin Shu & Kai Zhang & Ram Rajagopal & Nicolas Astier, 2016. "Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price," Papers 1701.02646, arXiv.org.
    3. Mo, Hua-Dong & Li, Yan-Fu & Zio, Enrico, 2016. "A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks," Applied Energy, Elsevier, vol. 183(C), pages 805-822.
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    5. 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.
    6. O’Neill, Zheng & O’Neill, Charles, 2016. "Development of a probabilistic graphical model for predicting building energy performance," Applied Energy, Elsevier, vol. 164(C), pages 650-658.
    7. Konstantin Hopf & Mariya Sodenkamp & Thorsten Staake, 2018. "Enhancing energy efficiency in the residential sector with smart meter data analytics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 453-473, November.
    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.
    9. Martin Neubert & Oliver Gnepper & Oliver Mey & André Schneider, 2022. "Detection of Electric Vehicles and Photovoltaic Systems in Smart Meter Data," Energies, MDPI, vol. 15(13), pages 1-15, July.
    10. Fayez Alanazi & Talal Obaid Alshammari & Abdelhalim Azam, 2023. "Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities," Sustainability, MDPI, vol. 15(22), pages 1-23, November.

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