IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i9p3409-d810128.html
   My bibliography  Save this article

Forecasting Charging Point Occupancy Using Supervised Learning Algorithms

Author

Listed:
  • Adrian Ostermann

    (FfE Munich, 80995 München, Germany
    School of Engineering and Design, Technical University of Munich (TUM), 80333 München, Germany)

  • Yann Fabel

    (FfE Munich, 80995 München, Germany)

  • Kim Ouan

    (Faculty of Informatics, Technical University of Munich (TUM), 80333 München, Germany)

  • Hyein Koo

    (FfE Munich, 80995 München, Germany
    Faculty of Informatics, Technical University of Munich (TUM), 80333 München, Germany)

Abstract

The prediction of charging point occupancy enables electric vehicle users to better plan their charging processes and thus promotes the acceptance of electromobility. The study uses Adaptive Charging Network data to investigate a public and a workplace site for predicting individual charging station occupancy as well as overall site occupancy. Predicting individual charging point occupancy is formulated as a classification problem, while predicting total occupancy is formulated as a regression problem. The effects of different feature sets on the predictions are investigated, as well as whether a model trained on data of all charging points per site performs better than one trained on the data of a specific charging point. Reviewed studies so far, however, have failed to compare these two approaches to benchmarks, to use more than one algorithm, or to consider more than one site. Therefore, the following supervised machine-learning algorithms were applied for both tasks: linear and logistic regression, k-nearest neighbor, random forest, and XGBoost. Further, the model results are compared to three different naïve approaches which provide a robust benchmark, and the two training approaches were applied to two different sites. By adding features, the prediction quality can be increased considerably, which resulted in some models performing better than the naïve approaches. In general, models trained on data of all charging points of a site perform slightly better on median than models trained on individual charging points. In certain cases, however, individually trained models achieve the best results, while charging points with very low relative charging point occupancy can benefit from a model that has been trained on all data.

Suggested Citation

  • Adrian Ostermann & Yann Fabel & Kim Ouan & Hyein Koo, 2022. "Forecasting Charging Point Occupancy Using Supervised Learning Algorithms," Energies, MDPI, vol. 15(9), pages 1-23, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3409-:d:810128
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/9/3409/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/9/3409/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Sen & Zhao, Huiru, 2015. "Optimal site selection of electric vehicle charging station by using fuzzy TOPSIS based on sustainability perspective," Applied Energy, Elsevier, vol. 158(C), pages 390-402.
    2. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. Fanchao Liao & Eric Molin & Bert van Wee, 2017. "Consumer preferences for electric vehicles: a literature review," Transport Reviews, Taylor & Francis Journals, vol. 37(3), pages 252-275, May.
    4. 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.
    5. Sierzchula, William & Bakker, Sjoerd & Maat, Kees & van Wee, Bert, 2014. "The influence of financial incentives and other socio-economic factors on electric vehicle adoption," Energy Policy, Elsevier, vol. 68(C), pages 183-194.
    6. Ahmad Almaghrebi & Fares Aljuheshi & Mostafa Rafaie & Kevin James & Mahmoud Alahmad, 2020. "Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods," Energies, MDPI, vol. 13(16), pages 1-21, August.
    7. Mathiesen, B.V. & Lund, H. & Connolly, D. & Wenzel, H. & Østergaard, P.A. & Möller, B. & Nielsen, S. & Ridjan, I. & Karnøe, P. & Sperling, K. & Hvelplund, F.K., 2015. "Smart Energy Systems for coherent 100% renewable energy and transport solutions," Applied Energy, Elsevier, vol. 145(C), pages 139-154.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fescioglu-Unver, Nilgun & Yıldız Aktaş, Melike, 2023. "Electric vehicle charging service operations: A review of machine learning applications for infrastructure planning, control, pricing and routing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Muratori, Matteo & Kontou, Eleftheria & Eichman, Joshua, 2019. "Electricity rates for electric vehicle direct current fast charging in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    2. Saiful Hasan & Terje Andreas Mathisen, 2020. "Policy measures for electric vehicle adoption. A review of evidence from Norway and China," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 0(1), pages 25-46.
    3. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    4. Gao, Jiong & Ma, Shoufeng & Zou, Hongyang & Du, Huibin, 2023. "How does population agglomeration influence the adoption of new energy vehicles? Evidence from 290 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    6. Jose Esteves & Daniel Alonso-Martínez & Guillermo de Haro, 2021. "Profiling Spanish Prospective Buyers of Electric Vehicles Based on Demographics," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    7. Katarzyna Chruzik & Marzena Graboń-Chałupczak, 2021. "The Concept of Safety Management in the Electromobility Development Strategy," Energies, MDPI, vol. 14(9), pages 1-17, April.
    8. 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.
    9. Ledna, Catherine & Muratori, Matteo & Brooker, Aaron & Wood, Eric & Greene, David, 2022. "How to support EV adoption: Tradeoffs between charging infrastructure investments and vehicle subsidies in California," Energy Policy, Elsevier, vol. 165(C).
    10. Vytautas Palevičius & Askoldas Podviezko & Henrikas Sivilevičius & Olegas Prentkovskis, 2018. "Decision-Aiding Evaluation of Public Infrastructure for Electric Vehicles in Cities and Resorts of Lithuania," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    11. Zheng, Xuemei & Menezes, Flavio & Zheng, Xiaofeng & Wu, Chengkuan, 2022. "An empirical assessment of the impact of subsidies on EV adoption in China: A difference-in-differences approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 162(C), pages 121-136.
    12. Athanasios Paraskevas & Dimitrios Aletras & Antonios Chrysopoulos & Antonios Marinopoulos & Dimitrios I. Doukas, 2022. "Optimal Management for EV Charging Stations: A Win–Win Strategy for Different Stakeholders Using Constrained Deep Q-Learning," Energies, MDPI, vol. 15(7), pages 1-24, March.
    13. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    14. Santos, Georgina & Rembalski, Sebastian, 2021. "Do electric vehicles need subsidies in the UK?," Energy Policy, Elsevier, vol. 149(C).
    15. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.
    16. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    17. Rotaris, Lucia & Giansoldati, Marco & Scorrano, Mariangela, 2021. "The slow uptake of electric cars in Italy and Slovenia. Evidence from a stated-preference survey and the role of knowledge and environmental awareness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 1-18.
    18. Austmann, Leonhard M., 2021. "Drivers of the electric vehicle market: A systematic literature review of empirical studies," Finance Research Letters, Elsevier, vol. 41(C).
    19. Jaiswal, Deepak & Kaushal, Vikrant & Kant, Rishi & Kumar Singh, Pankaj, 2021. "Consumer adoption intention for electric vehicles: Insights and evidence from Indian sustainable transportation," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    20. Elena Higueras-Castillo & Sebastian Molinillo & J. Andres Coca-Stefaniak & Francisco Liébana-Cabanillas, 2020. "Potential Early Adopters of Hybrid and Electric Vehicles in Spain—Towards a Customer Profile," Sustainability, MDPI, vol. 12(11), pages 1-18, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3409-:d:810128. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.