Short-term electric vehicle charging demand prediction: A deep learning approach

Shengyou Wang, Chengxiang Zhuge, Chunfu Shao, Pinxi Wang, Xiong Yang and Shiqi Wang

Applied Energy, 2023, vol. 340, issue C, No S0306261923003963

Abstract: Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model’s prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better.

Keywords: Electric vehicle; Charging demand prediction; Long short-term memory neural network; Trajectory data (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (11)

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DOI: 10.1016/j.apenergy.2023.121032

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