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Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

Author

Listed:
  • Jiaan Zhang

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Chenyu Liu

    (College of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China)

  • Leijiao Ge

    (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China)

Abstract

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2633-:d:786673
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    References listed on IDEAS

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    Cited by:

    1. Fangze Zhou & Hui Zhou & Zhaoyan Li & Kai Zhao, 2022. "Multi-Step Ahead Short-Term Electricity Load Forecasting Using VMD-TCN and Error Correction Strategy," Energies, MDPI, vol. 15(15), pages 1-18, July.
    2. Leijiao Ge & Jun Yan & Yonghui Sun & Zhongguan Wang, 2022. "Situational Awareness for Smart Distribution Systems," Energies, MDPI, vol. 15(11), pages 1-3, June.

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