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پنجمين دوره كنفرانس بین‌المللی اينترنت اشيا و كاربردها
The 5th International Conference on Internet of Things and Its Application

:: A Survey on Deep Learning Applications for Electric Vehicles in Micro Grids


A Survey on Deep Learning Applications for Electric Vehicles in Micro Grids

 

Pooya Tahmasebi Zadeh

Department of Computer Engineering

Faculty of Engineering, Lorestan

University Khorramabad, Iran

Email: mrpooya1@gmail.com

Maryam Joudaki

Faculty of Computer Engineering

Najafabad Branch, Islamic Azad

University of Najafabad, Iran

Email: mary.joudaki@gmail.com

 

Alireza Ansari

Department of Computer Engineering

and IT, Shiraz University of

Technology, Shiraz, Iran

Email: gkodaansari@gmail.com

AbstractDue to the increasing demand for energy and the environmental and economic challenges of reducing air pollution, while a large portion of the energy required comes from fossil fuels, distributed renewable energy generation systems seem to be viable solutions to this problem. Micro Grids are considered as the central part of the distributed generation system's body, while their management and control are of great importance. In recent years, the management and control of Micro Grids, their connection time to the leading network, and their disconnection time have been considered by many researchers since the optimization of these materials directly impacts economic benefits and consumer costs. In the interest of electric vehicles' nature and characteristics, they are connected to the power grid at the distribution level. The set of electric vehicles that are connected to the Micro Grids can cause problems for the grid to operate, such as voltage drops, increased losses, and unwanted clockwork during off-peak hours. In the last two years, deep learning methods have been used to address these challenges and other security and management challenges. This article surveys the applications of Deep Learning-based methods in Electric vehicles as a part of the Micro Grid.

 

Keywords-component; Electric Vehicle Micro Grid; Deep Learning; CNN.

 






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