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The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Author

Listed:
  • Yiqi Lu

    (Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China)

  • Yongpan Li

    (Shenzhen Power Supply Co. Ltd., Luohu District, Shenzhen 518001, China)

  • Da Xie

    (Shanghai Jiao Tong University, Minhang District, Shanghai 200240, China)

  • Enwei Wei

    (Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China)

  • Xianlu Bao

    (Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China)

  • Huafeng Chen

    (Shenzhen Power Supply Co. Ltd., Luohu District, Shenzhen 518001, China)

  • Xiancheng Zhong

    (Shenzhen Comtop Information Technology Co. Ltd., Shenzhen 518034, China)

Abstract

To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3207-:d:183837
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    3. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    4. Zhou, Kaile & Hu, Dingding & Li, Fangyi, 2022. "Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data," Transport Policy, Elsevier, vol. 125(C), pages 164-178.
    5. Vinay Simha Reddy Tappeta & Bhargav Appasani & Suprava Patnaik & Taha Selim Ustun, 2022. "A Review on Emerging Communication and Computational Technologies for Increased Use of Plug-In Electric Vehicles," Energies, MDPI, vol. 15(18), pages 1-26, September.
    6. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
    7. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    8. Juncheng Zhu & Zhile Yang & Monjur Mourshed & Yuanjun Guo & Yimin Zhou & Yan Chang & Yanjie Wei & Shengzhong Feng, 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches," Energies, MDPI, vol. 12(14), pages 1-19, July.
    9. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    10. Wang, Shengyou & Zhuge, Chengxiang & Shao, Chunfu & Wang, Pinxi & Yang, Xiong & Wang, Shiqi, 2023. "Short-term electric vehicle charging demand prediction: A deep learning approach," Applied Energy, Elsevier, vol. 340(C).
    11. 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).
    12. Changzhi Li & Dandan Liu & Mao Wang & Hanlin Wang & Shuai Xu, 2023. "Detection of Outliers in Time Series Power Data Based on Prediction Errors," Energies, MDPI, vol. 16(2), pages 1-19, January.

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