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

Energies

Chenyu Liu¹  Leijiao Ge²  Jiaan Zhang³ 

[1] College of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300401, China;Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China;State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China;

关键词: electric vehicle;    short-term load forecasting;    convolutional neural network;    temporal convolutional network;    climate factors;    correlation analysis;   

DOI  :  10.3390/en15072633

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown