IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1534-d114003.html
   My bibliography  Save this article

An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations

Author

Listed:
  • Seongpil Cheon

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

  • Suk-Ju Kang

    (Department of Electronic Engineering, Sogang University, Seoul 04107, Korea)

Abstract

With the rising demand for electric vehicles, the number of electric vehicle charging stations is increasing. Therefore, real-time monitoring of how the power consumption by charging stations affects the load on the peripheral power grid is important. However, related organizations generally do not provide actual power consumption data in real time, and only limited information, such as the charging time, is provided. Therefore, it is difficult to calculate and predict the power load in real time. In this paper, we propose a new model for estimating the electric power consumption from the supplied information, i.e., the charging time and the number of charging involved. The experimental results show that by displaying this information on a map, it is possible to visually monitor the electric power consumption of the charging stations with an accuracy rate of about 86%. Finally, the proposed system can be used to relocate and select the location of vehicle charging stations.

Suggested Citation

  • Seongpil Cheon & Suk-Ju Kang, 2017. "An Electric Power Consumption Analysis System for the Installation of Electric Vehicle Charging Stations," Energies, MDPI, vol. 10(10), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1534-:d:114003
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1534/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1534/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cedric De Cauwer & Joeri Van Mierlo & Thierry Coosemans, 2015. "Energy Consumption Prediction for Electric Vehicles Based on Real-World Data," Energies, MDPI, vol. 8(8), pages 1-21, August.
    2. Rob J. Hyndman, 2006. "Another Look at Forecast Accuracy Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 43-46, June.
    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. Namhyun Ahn & So Yeon Jo & Suk-Ju Kang, 2019. "Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station," Energies, MDPI, vol. 12(6), pages 1-18, March.
    2. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.
    3. Bong-Gi Choi & Byeong-Chan Oh & Sungyun Choi & Sung-Yul Kim, 2020. "Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method," Energies, MDPI, vol. 13(7), pages 1-20, April.
    4. Munseok Chang & Sungwoo Bae & Gilhwan Cha & Jaehyun Yoo, 2021. "Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-17, December.
    5. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    6. Golsefidi, Atefeh Hemmati & Hüttel, Frederik Boe & Peled, Inon & Samaranayake, Samitha & Pereira, Francisco Câmara, 2023. "A joint machine learning and optimization approach for incremental expansion of electric vehicle charging infrastructure," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(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. Namhyun Ahn & So Yeon Jo & Suk-Ju Kang, 2019. "Constraint-Aware Electricity Consumption Estimation for Prevention of Overload by Electric Vehicle Charging Station," Energies, MDPI, vol. 12(6), pages 1-18, March.
    2. Michael Vössing & Niklas Kühl & Matteo Lind & Gerhard Satzger, 2022. "Designing Transparency for Effective Human-AI Collaboration," Information Systems Frontiers, Springer, vol. 24(3), pages 877-895, June.
    3. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    4. Corani, Giorgio & Azzimonti, Dario & Rubattu, Nicolò, 2024. "Probabilistic reconciliation of count time series," International Journal of Forecasting, Elsevier, vol. 40(2), pages 457-469.
    5. Katsaprakakis, Dimitris Al & Voumvoulakis, Manolis, 2018. "A hybrid power plant towards 100% energy autonomy for the island of Sifnos, Greece. Perspectives created from energy cooperatives," Energy, Elsevier, vol. 161(C), pages 680-698.
    6. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    7. Wang, Hua & Zhao, De & Meng, Qiang & Ong, Ghim Ping & Lee, Der-Horng, 2020. "Network-level energy consumption estimation for electric vehicles considering vehicle and user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 30-46.
    8. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    9. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E. & Fildes, Robert & Goodwin, Paul, 2009. "The effects of integrating management judgement into intermittent demand forecasts," International Journal of Production Economics, Elsevier, vol. 118(1), pages 72-81, March.
    10. Cox, Brian & Bauer, Christian & Mendoza Beltran, Angelica & van Vuuren, Detlef P. & Mutel, Christopher L., 2020. "Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios," Applied Energy, Elsevier, vol. 269(C).
    11. Manfred Dollinger & Gerhard Fischerauer, 2023. "Physics-Based Prediction for the Consumption and Emissions of Passenger Vehicles and Light Trucks up to 2050," Energies, MDPI, vol. 16(8), pages 1-29, April.
    12. Yunna Wu & Meng Yang & Haobo Zhang & Kaifeng Chen & Yang Wang, 2016. "Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method," Energies, MDPI, vol. 9(3), pages 1-20, March.
    13. Sprangers, Olivier & Schelter, Sebastian & de Rijke, Maarten, 2023. "Parameter-efficient deep probabilistic forecasting," International Journal of Forecasting, Elsevier, vol. 39(1), pages 332-345.
    14. Muhammed Alhanouti & Frank Gauterin, 2024. "A Generic Model for Accurate Energy Estimation of Electric Vehicles," Energies, MDPI, vol. 17(2), pages 1-21, January.
    15. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    16. Meoli, Michele & Vismara, Silvio, 2022. "Machine-learning forecasting of successful ICOs," Journal of Economics and Business, Elsevier, vol. 121(C).
    17. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    18. Nils Hooftman & Luis Oliveira & Maarten Messagie & Thierry Coosemans & Joeri Van Mierlo, 2016. "Environmental Analysis of Petrol, Diesel and Electric Passenger Cars in a Belgian Urban Setting," Energies, MDPI, vol. 9(2), pages 1-24, January.
    19. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
    20. Karol Tucki & Olga Orynycz & Antoni Świć & Mateusz Mitoraj-Wojtanek, 2019. "The Development of Electromobility in Poland and EU States as a Tool for Management of CO 2 Emissions," Energies, MDPI, vol. 12(15), pages 1-22, July.

    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:10:y:2017:i:10:p:1534-:d:114003. 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.