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Electric vehicle charging load forecasting: A comparative study of deep learning approaches

Zhu, Juncheng, Yang, Zhile, Mourshed, Monjur, Guo, Yuanjun, Zhou, Yimin, Chang, Yan, Wei, Yanjie and Feng, Shengzhong 2019. Electric vehicle charging load forecasting: A comparative study of deep learning approaches. Energies 12 (14) , 2692. 10.3390/en12142692

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Abstract

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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: MDPI
ISSN: 1996-1073
Date of First Compliant Deposit: 30 August 2019
Date of Acceptance: 5 July 2019
Last Modified: 30 Aug 2019 11:15
URI: http://orca.cf.ac.uk/id/eprint/125158

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