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

Jiaan Zhang, Chenyu Liu and Leijiao Ge
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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

Energies, 2022, vol. 15, issue 7, 1-25

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.

Keywords: electric vehicle; short-term load forecasting; convolutional neural network; temporal convolutional network; climate factors; correlation analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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