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Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches.

Authors :
Zhu, Juncheng
Yang, Zhile
Guo, Yuanjun
Zhang, Jiankang
Yang, Huikun
Source :
Applied Sciences (2076-3417); 5/15/2019, Vol. 9 Issue 9, p1723, 12p
Publication Year :
2019

Abstract

Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
9
Issue :
9
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
137306996
Full Text :
https://doi.org/10.3390/app9091723