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Article

Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches

1
School of Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3
School of Engineering, Cardiff University, Cardiff CF24 3AA, UK
4
School of Software Engineering, University of Science and Technology of China, Hefei 230026, China
*
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2692; https://doi.org/10.3390/en12142692
Received: 19 June 2019 / Revised: 4 July 2019 / Accepted: 5 July 2019 / Published: 13 July 2019
Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model. View Full-Text
Keywords: load forecasting; LSTM; electric vehicles; deep learning load forecasting; LSTM; electric vehicles; deep learning
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MDPI and ACS Style

Zhu, J.; Yang, Z.; Mourshed, M.; Guo, Y.; Zhou, Y.; Chang, Y.; Wei, Y.; Feng, S. Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches. Energies 2019, 12, 2692. https://doi.org/10.3390/en12142692

AMA Style

Zhu J, Yang Z, Mourshed M, Guo Y, Zhou Y, Chang Y, Wei Y, Feng S. Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches. Energies. 2019; 12(14):2692. https://doi.org/10.3390/en12142692

Chicago/Turabian Style

Zhu, Juncheng, Zhile Yang, Monjur Mourshed, Yuanjun Guo, Yimin Zhou, Yan Chang, Yanjie Wei, and Shengzhong Feng. 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches" Energies 12, no. 14: 2692. https://doi.org/10.3390/en12142692

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