Open Access

ARTICLE

Prediction of EV charging behavior using BOA-based deep residual attention network

Jothi Prabha Appadurai¹, Dr. Rajesh T², R. Yugha³, Rashel Sarkar⁴, Arunadevi Thirumalraj⁵, Balasubramanian Prabhu kavin⁶, Dr. Gan Hong Seng⁷

1 Department of CSE(Networks); Kakatiya institute of technology and science, Warangal - 506015, Telangana.
2 Professor, Department of Electrical and Electronics Engineering, Malla Reddy Engineering College, Hyderabad, India.
3 Assistant Professor, School of Computing, Sathyabama Institute of Science and Technology, Chennai 600119.
4 Department of Computer Science and Engineering, The Assam Royal Global University,kamrup, Assam-781035.
5 Department of Computer Science Engineering, K.Ramakrishnan College of Technology, Trichy, India.
6 Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu- 603203.
7 School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong - Liverpool University, Suzhou, Jiangsu, P.R. China 215400.

* Corresponding Authors: Jothi Prabha Appadurai (), Dr. Rajesh T (), R. Yugha (), Rashel Sarkar (), Arunadevi Thirumalraj (), Balasubramanian Prabhu kavin (), Dr. Gan Hong Seng ()

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 2024, 40(2), 1-9. https://doi.org/10.23967/j.rimni.2024.02.002

Received 29 February 2024; Accepted 25 April 2024; Issue published 03 May 2024

Abstract

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

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