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

Authors :
Zhu, Juncheng
Yang, Zhile
Mourshed, Monjur
Guo, Yuanjun
Zhou, Yimin
Chang, Yan
Wei, Yanjie
Feng, Shengzhong
Source :
Energies (19961073); 7/15/2019, Vol. 12 Issue 14, p2692-2692, 1p
Publication Year :
2019

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961073
Volume :
12
Issue :
14
Database :
Complementary Index
Journal :
Energies (19961073)
Publication Type :
Academic Journal
Accession number :
137681631
Full Text :
https://doi.org/10.3390/en12142692