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Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks

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  • Ma, Tai-Yu
  • Faye, Sébastien

Abstract

Public charging station occupancy prediction plays key importance in developing a smart charging strategy to reduce electric vehicle (EV) operator and user inconvenience. However, existing studies are mainly based on conventional econometric or time series methodologies with limited accuracy. We propose a new mixed long short-term memory neural network incorporating both historical charging state sequences and time-related features for multistep discrete charging occupancy state prediction. Unlike the existing LSTM networks, the proposed model separates different types of features and handles them differently with mixed neural network architecture. The model is compared to a number of state-of-the-art machine learning and deep learning approaches based on the EV charging data obtained from the open data portal of the city of Dundee, UK. The results show that the proposed method produces very accurate predictions (99.99% and 81.87% for 1 step (10 min) and 6 steps (1 h) ahead, respectively, and outperforms the benchmark approaches significantly (+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). A sensitivity analysis is conducted to evaluate the impact of the model parameters on prediction accuracy.

Suggested Citation

  • Ma, Tai-Yu & Faye, Sébastien, 2022. "Multistep electric vehicle charging station occupancy prediction using hybrid LSTM neural networks," Energy, Elsevier, vol. 244(PB).
    • Handle: RePEc:eee:energy:v:244:y:2022:i:pb:s0360544222001207
    DOI: 10.1016/j.energy.2022.123217
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    References listed on IDEAS

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    Cited by:

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    4. Ahmed M. Abed & Ali AlArjani, 2022. "The Neural Network Classifier Works Efficiently on Searching in DQN Using the Autonomous Internet of Things Hybridized by the Metaheuristic Techniques to Reduce the EVs’ Service Scheduling Time," Energies, MDPI, vol. 15(19), pages 1-25, September.
    5. Sayed, Mohammad Ali & Ghafouri, Mohsen & Atallah, Ribal & Debbabi, Mourad & Assi, Chadi, 2024. "Grid Chaos: An uncertainty-conscious robust dynamic EV load-altering attack strategy on power grid stability," Applied Energy, Elsevier, vol. 363(C).
    6. Tian, Chenlu & Liu, Yechun & Zhang, Guiqing & Yang, Yalong & Yan, Yi & Li, Chengdong, 2024. "Transfer learning based hybrid model for power demand prediction of large-scale electric vehicles," Energy, Elsevier, vol. 300(C).
    7. Li, Tao & Liu, Xiangyu & Li, Guannan & Wang, Xing & Ma, Jiangqiaoyu & Xu, Chengliang & Mao, Qianjun, 2024. "A systematic review and comprehensive analysis of building occupancy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    8. Yao, Fang & He, Wenxuan & Wu, Youxi & Ding, Fei & Meng, Defang, 2022. "Remaining useful life prediction of lithium-ion batteries using a hybrid model," Energy, Elsevier, vol. 248(C).
    9. Sayed, Mohammad Ali & Ghafouri, Mohsen & Atallah, Ribal & Debbabi, Mourad & Assi, Chadi, 2023. "Protecting the future grid: An electric vehicle robust mitigation scheme against load altering attacks on power grids," Applied Energy, Elsevier, vol. 350(C).

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