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A deep learning based approach for predicting the demand of electric vehicle charge

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Abstract

Predicting the demand for Electric Vehicle charging energy is essential to increase utilization for the company, reduce costs for both car owners and the company and alleviate the burden on the electric grid stations. However, many factors may affect energy consumption at the station level, such as the growing popularity of EVs, time of day plugin, workday, holidays, random consumption, etc. To overcome the above challenges regarding avoiding overcharge, better managing dispatching stations, reducing energy wastage, we perform a comprehensive data analysis on EV charging stations and propose a novel deep learning based approach. Our research is based on the charging data obtained from a Chinese energy service provider, including the stations’ charging process and geographic information. In the forecasting part, we propose Temporal Encoder-Decoder +LSTM (T-LSTM-Enc) Concatenated with Temporal LSTM (T-LSTM-Ori-TimeFeatures) which aim to address the issue of charging demand prediction. The T-LSTM-Enc pre-trains the data to extract hidden relationships, and the T-LSTM-Ori-TimeFeatures capture the time features impacting the change on the charging data. We build our approach using temporal dependencies to apprehend the short-term, long-term, and trend characteristics for charging demand prediction. To show the efficiency of the proposed method, we evaluate our model using the two datasets for energy consumption of EV charging stations, and the results show that our approach gives promising and good performance.

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Acknowledgements

This work is supported in part by the National Key Research and Development Program of China (No. 2021ZD0112400), and also in part by the National Natural Science Foundation of China under Grant U1811463.

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  1. Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

    Mekkaoui Djamel Eddine & Yanming Shen

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Correspondence to Yanming Shen.

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Eddine, M.D., Shen, Y. A deep learning based approach for predicting the demand of electric vehicle charge. J Supercomput 78, 14072–14095 (2022). https://doi.org/10.1007/s11227-022-04428-0

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