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Forecasting Charging Demand of Electric Vehicles Using Time-Series Models

Author

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  • Yunsun Kim

    (Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea)

  • Sahm Kim

    (Department of Applied Statistics, Chung-Ang University, Seoul 06974, Korea)

Abstract

This study compared the methods used to forecast increases in power consumption caused by the rising popularity of electric vehicles (EVs). An excellent model for each region was proposed using multiple scaled geographical datasets over two years. EV charging volumes are influenced by various factors, including the condition of a vehicle, the battery’s state-of-charge (SOC), and the distance to the destination. However, power suppliers cannot easily access this information due to privacy issues. Despite a lack of individual information, this study compared various modeling techniques, including trigonometric exponential smoothing state space (i.e., Trigonometric, Box–Cox, Auto-Regressive-Moving-Average (ARMA), Trend, and Seasonality (TBATS)), autoregressive integrated moving average (ARIMA), artificial neural networks (ANN), and long short-term memory (LSTM) modeling, based on past values and exogenous variables. The effect of exogenous variables was evaluated in macro- and micro-scale geographical areas, and the importance of historic data was verified. The basic statistics regarding the number of charging stations and the volume of charging in each region are expected to aid the formulation of a method that can be used by power suppliers.

Suggested Citation

  • Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:5:p:1487-:d:513289
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    References listed on IDEAS

    as
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    Cited by:

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    2. Paolo Lazzeroni & Brunella Caroleo & Maurizio Arnone & Cristiana Botta, 2021. "A Simplified Approach to Estimate EV Charging Demand in Urban Area: An Italian Case Study," Energies, MDPI, vol. 14(20), pages 1-18, October.
    3. Genov, Evgenii & Cauwer, Cedric De & Kriekinge, Gilles Van & Coosemans, Thierry & Messagie, Maarten, 2024. "Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods," Applied Energy, Elsevier, vol. 353(PA).
    4. Anna Borucka, 2023. "Seasonal Methods of Demand Forecasting in the Supply Chain as Support for the Company’s Sustainable Growth," Sustainability, MDPI, vol. 15(9), pages 1-21, April.
    5. Abdullah-Al-Nahid, Syed & Jamal, Taskin & Aziz, Tareq & Bhuiyan, Ashraf Hossain & Khan, Tafsir Ahmed, 2023. "Additive linear modelling and genetic algorithm based electric vehicle outlook and policy formulation for decarbonizing the future transport sector of Bangladesh," Transport Policy, Elsevier, vol. 136(C), pages 21-46.
    6. Young-Eun Jeon & Suk-Bok Kang & Jung-In Seo, 2022. "Hybrid Predictive Modeling for Charging Demand Prediction of Electric Vehicles," Sustainability, MDPI, vol. 14(9), pages 1-15, April.
    7. Sakar Hasan Hamza & Qingna Li, 2023. "The Dynamics of US Gasoline Demand and Its Prediction: An Extended Dynamic Model Averaging Approach," Energies, MDPI, vol. 16(12), pages 1-13, June.
    8. Klemen Deželak & Klemen Sredenšek & Sebastijan Seme, 2023. "Energy Consumption and Grid Interaction Analysis of Electric Vehicles Based on Particle Swarm Optimisation Method," Energies, MDPI, vol. 16(14), pages 1-15, July.
    9. 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).

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