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Forecasting flexibility of charging of electric vehicles: Tree and cluster-based methods

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  • Genov, Evgenii
  • Cauwer, Cedric De
  • Kriekinge, Gilles Van
  • Coosemans, Thierry
  • Messagie, Maarten

Abstract

Scheduling electric vehicle charging sessions allows to aggregate flexibility in order to minimize energy costs and reduce congestion in the electricity grid. Existing research shows that user input for energy and parking duration does not serve as a reliable prediction. The study presents an evaluation of the forecast error and computational performance of the different models. Two main methods are investigated: tree-based (gradient boosted trees LightGBM) and cluster-based (Gaussian mixture model). We also present a novel dynamic cluster-based method, the Similar Sessions method, which employs the similarity between charging sessions based on numerical variables. The results highlight the importance of selecting the forecast model influenced by the availability of training data. The effect of user registration on the accuracy of the forecast is investigated. The tests are run using ACN-Data dataset of Electric Vehicle charging sessions in California, United States. While underperforming on a small dataset with a short look-back period, tree-based methods show superiority while the charging data are accumulating. The Similar Sessions method shows superior accuracy under various data availability conditions. The proposed method requires no prior training, but has slower computational performance in deployment.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923013338
    DOI: 10.1016/j.apenergy.2023.121969
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    References listed on IDEAS

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    1. Bojer, Casper Solheim & Meldgaard, Jens Peder, 2021. "Kaggle forecasting competitions: An overlooked learning opportunity," International Journal of Forecasting, Elsevier, vol. 37(2), pages 587-603.
    2. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    3. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    4. de la Torre, S. & Aguado, J.A. & Sauma, E., 2023. "Optimal scheduling of ancillary services provided by an electric vehicle aggregator," Energy, Elsevier, vol. 265(C).
    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. Yukai Chen & Khaled Sidahmed Sidahmed Alamin & Daniele Jahier Pagliari & Sara Vinco & Enrico Macii & Massimo Poncino, 2020. "Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization," Energies, MDPI, vol. 13(16), pages 1-19, August.
    7. Yiqi Lu & Yongpan Li & Da Xie & Enwei Wei & Xianlu Bao & Huafeng Chen & Xiancheng Zhong, 2018. "The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load," Energies, MDPI, vol. 11(11), pages 1-16, November.
    8. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
    9. Gilanifar, Mostafa & Parvania, Masood, 2021. "Clustered multi-node learning of electric vehicle charging flexibility," Applied Energy, Elsevier, vol. 282(PB).
    Full references (including those not matched with items on IDEAS)

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