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Predicting popularity of EV charging infrastructure from GIS data

Author

Listed:
  • Milan Straka
  • Pasquale De Falco
  • Gabriella Ferruzzi
  • Daniela Proto
  • Gijs van der Poel
  • Shahab Khormali
  • v{L}ubov{s} Buzna

Abstract

The availability of charging infrastructure is essential for large-scale adoption of electric vehicles (EV). Charging patterns and the utilization of infrastructure have consequences not only for the energy demand, loading local power grids but influence the economic returns, parking policies and further adoption of EVs. We develop a data-driven approach that is exploiting predictors compiled from GIS data describing the urban context and urban activities near charging infrastructure to explore correlations with a comprehensive set of indicators measuring the performance of charging infrastructure. The best fit was identified for the size of the unique group of visitors (popularity) attracted by the charging infrastructure. Consecutively, charging infrastructure is ranked by popularity. The question of whether or not a given charging spot belongs to the top tier is posed as a binary classification problem and predictive performance of logistic regression regularized with an l-1 penalty, random forests and gradient boosted regression trees is evaluated. Obtained results indicate that the collected predictors contain information that can be used to predict the popularity of charging infrastructure. The significance of predictors and how they are linked with the popularity are explored as well. The proposed methodology can be used to inform charging infrastructure deployment strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:1910.02498
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    References listed on IDEAS

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    1. Alexandre Lucas & Giuseppe Prettico & Marco Giacomo Flammini & Evangelos Kotsakis & Gianluca Fulli & Marcelo Masera, 2018. "Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis," Energies, MDPI, vol. 11(7), pages 1-18, July.
    2. Greene, D.L. & Boudreaux, P.R. & Dean, D.J. & Fulkerson, W. & Gaddis, A.L. & Graham, R.L. & Graves, R.L. & Hopson, J.L. & Hughes, P. & Lapsa, M.V. & Mason, T.E. & Standaert, R.F. & Wilbanks, T.J. & Zu, 2010. "The importance of advancing technology to America's energy goals," Energy Policy, Elsevier, vol. 38(8), pages 3886-3890, August.
    3. Helmus, J.R. & Spoelstra, J.C. & Refa, N. & Lees, M. & van den Hoed, R., 2018. "Assessment of public charging infrastructure push and pull rollout strategies: The case of the Netherlands," Energy Policy, Elsevier, vol. 121(C), pages 35-47.
    4. Makena Coffman & Paul Bernstein & Sherilyn Wee, 2017. "Electric vehicles revisited: a review of factors that affect adoption," Transport Reviews, Taylor & Francis Journals, vol. 37(1), pages 79-93, January.
    5. De Gennaro, Michele & Paffumi, Elena & Martini, Giorgio, 2015. "Customer-driven design of the recharge infrastructure and Vehicle-to-Grid in urban areas: A large-scale application for electric vehicles deployment," Energy, Elsevier, vol. 82(C), pages 294-311.
    6. Rahman, Imran & Vasant, Pandian M. & Singh, Balbir Singh Mahinder & Abdullah-Al-Wadud, M. & Adnan, Nadia, 2016. "Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1039-1047.
    7. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    8. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    9. Ren, Xianqiang & Zhang, Huiming & Hu, Ruohan & Qiu, Yueming, 2019. "Location of electric vehicle charging stations: A perspective using the grey decision-making model," Energy, Elsevier, vol. 173(C), pages 548-553.
    10. Ajanovic, Amela & Haas, Reinhard, 2016. "Dissemination of electric vehicles in urban areas: Major factors for success," Energy, Elsevier, vol. 115(P2), pages 1451-1458.
    11. 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.
    12. Davidov, Sreten & Pantoš, Miloš, 2017. "Planning of electric vehicle infrastructure based on charging reliability and quality of service," Energy, Elsevier, vol. 118(C), pages 1156-1167.
    13. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
    14. Tao, Ye & Huang, Miaohua & Yang, Lan, 2018. "Data-driven optimized layout of battery electric vehicle charging infrastructure," Energy, Elsevier, vol. 150(C), pages 735-744.
    15. Asamer, Johannes & Reinthaler, Martin & Ruthmair, Mario & Straub, Markus & Puchinger, Jakob, 2016. "Optimizing charging station locations for urban taxi providers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 85(C), pages 233-246.
    16. Alexandre Lucas & Ricardo Barranco & Nazir Refa, 2019. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions," Energies, MDPI, vol. 12(2), pages 1-17, January.
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    Cited by:

    1. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.

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