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Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network

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  • Yunyan Li

    (Department of Economic Management, North China Electric Power University, Baoding 071000, China)

  • Yuansheng Huang

    (Department of Economic Management, North China Electric Power University, Baoding 071000, China)

  • Meimei Zhang

    (Department of Economic Management, North China Electric Power University, Baoding 071000, China)

Abstract

Accurate and stable prediction of short-term load for electric vehicle charging stations is of great significance in ensuring economical and safe operation of electric vehicle charging stations and power grids. In order to improve the accuracy and stability of short-term load forecasting for electric vehicle charging stations, an innovative prediction model based on a convolutional neural network and lion algorithm, improved by niche immunity, is proposed. Firstly, niche immunity is utilized to restrict over duplication of similar individuals, so as to ensure population diversity of lion algorithm, which improves the optimization performance of the lion algorithm significantly. The lion algorithm is then employed to search the optimal weights and thresholds of the convolutional neural network. Finally, a proposed short-term load forecasting method is established. After analyzing the load characteristics of the electric vehicle charging station, two cases in different locations and different months are selected to validate the proposed model. The results indicate that the new hybrid proposed model offers better accuracy, robustness, and generality in short-term load forecasting for electric vehicle charging stations.

Suggested Citation

  • Yunyan Li & Yuansheng Huang & Meimei Zhang, 2018. "Short-Term Load Forecasting for Electric Vehicle Charging Station Based on Niche Immunity Lion Algorithm and Convolutional Neural Network," Energies, MDPI, vol. 11(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1253-:d:146275
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    References listed on IDEAS

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    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. Seon Hyeog Kim & Gyul Lee & Gu-Young Kwon & Do-In Kim & Yong-June Shin, 2018. "Deep Learning Based on Multi-Decomposition for Short-Term Load Forecasting," Energies, MDPI, vol. 11(12), pages 1-17, December.
    3. Andu Dukpa & Boguslaw Butrylo & Bala Venkatesh, 2023. "Comparative Analysis and Optimal Operation of an On-Grid and Off-Grid Solar Photovoltaic-Based Electric Vehicle Charging Station," Energies, MDPI, vol. 16(24), pages 1-17, December.
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    5. Stavros Poniris & Anastasios I. Dounis, 2022. "Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm," Energies, MDPI, vol. 16(1), pages 1-16, December.
    6. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    7. Andu Dukpa & Boguslaw Butrylo, 2022. "MILP-Based Profit Maximization of Electric Vehicle Charging Station Based on Solar and EV Arrival Forecasts," Energies, MDPI, vol. 15(15), pages 1-14, August.
    8. Gilanifar, Mostafa & Parvania, Masood, 2021. "Clustered multi-node learning of electric vehicle charging flexibility," Applied Energy, Elsevier, vol. 282(PB).
    9. Liu, Jin-peng & Zhang, Teng-xi & Zhu, Jiang & Ma, Tian-nan, 2018. "Allocation optimization of electric vehicle charging station (EVCS) considering with charging satisfaction and distributed renewables integration," Energy, Elsevier, vol. 164(C), pages 560-574.
    10. Peng Guo & Jian Fu & XiYun Yang, 2018. "Condition Monitoring and Fault Diagnosis of Wind Turbines Gearbox Bearing Temperature Based on Kolmogorov-Smirnov Test and Convolutional Neural Network Model," Energies, MDPI, vol. 11(9), pages 1-16, August.
    11. Sanchari Deb, 2021. "Machine Learning for Solving Charging Infrastructure Planning Problems: A Comprehensive Review," Energies, MDPI, vol. 14(23), pages 1-19, November.
    12. 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).
    13. Sahar Koohfar & Wubeshet Woldemariam & Amit Kumar, 2023. "Performance Comparison of Deep Learning Approaches in Predicting EV Charging Demand," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    14. Bampos, Zafeirios N. & Laitsos, Vasilis M. & Afentoulis, Konstantinos D. & Vagropoulos, Stylianos I. & Biskas, Pantelis N., 2024. "Electric vehicles load forecasting for day-ahead market participation using machine and deep learning methods," Applied Energy, Elsevier, vol. 360(C).
    15. 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.
    16. Yunsun Kim & Sahm Kim, 2021. "Forecasting Charging Demand of Electric Vehicles Using Time-Series Models," Energies, MDPI, vol. 14(5), pages 1-16, March.
    17. 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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