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Feature Selection Modeling on Predicting EV Charging Station Coverage Rate in Southern California

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Advances in Simulation and Digital Human Modeling (AHFE 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1206))

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Abstract

This project emphasizes on the meso-level dynamics behind the selection of EV charger locations. The central argument of this article is that many socio-economic and demographic factors explain the distribution of EV charging stations in Southern California. Using EV station data from the US Department of Energy and 2017 ACS data, we adopt multiple model selection method to select best demographic features in predicting the coverage rate of EV charging stations in given zip code area. In sum, we hope to provide a succinct but accurate picture that explains dispersion of EV charging stations in the Southern Californian region.

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Notes

  1. 1.

    https://afdc.energy.gov/fuels/electricity_locations.html#/find/nearest?fuel=ELEC.

  2. 2.

    https://factfinder.census.gov/faces/nav/jsf/pages/download_center.xhtml.

  3. 3.

    The ten counties are: Imperial, Kern, Los Angeles, Orange, Riverside, San Bernardino, San Diego, Santa Barbara, San Luis Obispo, and Ventura.

  4. 4.

    This part is done using SAS due to its strength on handling missing datapoints in ACS data.

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Author information

Authors and Affiliations

  1. Claremont Graduate University, 150 E 10th St., Claremont, CA, 91711, USA

    Zining Yang

  2. Baylor University, 1301 S University Parks Dr., Waco, TX, 76706, USA

    Ruiqian Li

Authors
  1. Zining Yang
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  2. Ruiqian Li
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Corresponding author

Correspondence to Zining Yang .

Editor information

Editors and Affiliations

  1. RDRL-HRF-D, Building 519, U.S. Army Research Laboratory, Aberdeen Proving Ground, MD, USA

    Daniel N Cassenti

  2. Department of Product Development, Faculty of Design Sciences, University of Antwerp, Antwerp, Belgium

    Sofia Scataglini

  3. Biomedical Research & Environmental Sciences Division, NASA Lyndon Johnson Space Center, Houston, TX, USA

    Sudhakar L. Rajulu

  4. U.S. Army Research Laboratory, Orlando, FL, USA

    Julia L. Wright

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Yang, Z., Li, R. (2021). Feature Selection Modeling on Predicting EV Charging Station Coverage Rate in Southern California. In: Cassenti, D., Scataglini, S., Rajulu, S., Wright, J. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1206. Springer, Cham. https://doi.org/10.1007/978-3-030-51064-0_13

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