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LA-RCNN: Luong attention-recurrent- convolutional neural network for EV charging load prediction

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Abstract

This article explores the domain of accurate Electric Vehicle (EV) charge prediction, a crucial aspect of the energy consumption system. Predicting EV energy consumption is challenging due to the dynamic dependence and heterogeneity. Despite various approaches proposed in previous studies for intelligent charging, many models rely on limited inputs and ignore the non-linear interactivity between different time series. Moreover, to our knowledge, previous research has not considered the number of connected EVs during the charging procedure. This paper develops an attention-based recurrent convolutional neural network model (LA-RCNN) designed to forecast EV charging load using multivariate time series inputs, including meteorological data and the number of connected users. The proposed model incorporates multiplicative Luong Attention to identify temporal dependencies and correlations. Our objective is to predict the national charging load by considering the charging state and the number of plug-in EVs connected to various charging stations. Using real-world EV charging data from three Chinese cities, we demonstrate that the LA-RCNN model significantly enhances forecast accuracy compared to benchmark methods, reducing MAPE by 21.33% and RMSE by 18.73% as compared to LSTM models. These results highlight the importance of nonlinear attention-based architectures and diverse contextual data sources for effective EV load prediction.

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The datasets analyzed in this study are not publicly available to maintain the privacy of the Chinese company.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62276044, and also in part by the Innovation Foundation of Science and Technology of Dalian under Grant 2022JJ12SN052.

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Authors and Affiliations

  1. School of Computer Science and Technology, Dalian University of Technology, Dalian, China

    Djamel Eddine Mekkaoui & Yanming Shen

  2. School of Control Science and Engineering, Dalian University of Technology, Dalian, China

    Mohamed Amine Midoun

Authors
  1. Djamel Eddine Mekkaoui
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  2. Mohamed Amine Midoun
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  3. Yanming Shen
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Contributions

All authors contributed to the conceptualization of the study. Djamel Eddine Mekkaoui performed the methodology design and analysis. Experimentation and coding were carried out by Djamel Eddine Mekkaoui and Mohamed Amine Midoun. Funding acquisition was managed by Yanming Shen, who also supervised the study. The original manuscript was drafted by Djamel Eddine Mekkaoui and subsequently reviewed, edited, and approved by all authors.

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Correspondence to Yanming Shen.

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Mekkaoui, D.E., Midoun, M.A. & Shen, Y. LA-RCNN: Luong attention-recurrent- convolutional neural network for EV charging load prediction. Appl Intell 54, 4352–4369 (2024). https://doi.org/10.1007/s10489-024-05394-1

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