Deep Learning LSTM Recurrent Neural Network Model for Prediction of Electric Vehicle Charging Demand

Jaikumar Shanmuganathan, Aruldoss Albert Victoire (), Gobu Balraj and Amalraj Victoire
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Jaikumar Shanmuganathan: Department of Electrical & Electronics Engineering, Anna University Regional Campus Coimbatore, Coimbatore 641047, India
Aruldoss Albert Victoire: Department of Electrical & Electronics Engineering, Anna University Regional Campus Coimbatore, Coimbatore 641047, India
Gobu Balraj: Assistant Engineer, Tamilnadu Generation and Distribution Corporation Ltd., Tamilnadu 641012, India
Amalraj Victoire: Department of Computer Applications, Sri Manakula Vinayagar Engineering College, Pondicherry 605107, India

Sustainability, 2022, vol. 14, issue 16, 1-28

Abstract: The immense growth and penetration of electric vehicles has become a major component of smart transport systems; thereby decreasing the greenhouse gas emissions that pollute the environment. With the increased volumes of electric vehicles (EV) in the past few years, the charging demand of these vehicles has also become an immediate requirement. Due to which, the prediction of the demand of electric vehicle charging is of key importance so that it minimizes the burden on the electric grids and also offers reduced costs of charging. In this research study, an attempt is made to develop a novel deep learning (DL)-based long-short term memory (LSTM) recurrent neural network predictor model to carry out the forecasting of electric vehicle charging demand. The parameters of the new deep long-short term memory (DLSTM) neural predictor model are tuned for its optimal values using the classic arithmetic optimization algorithm (AOA) and the input time series data are decomposed so as to maintain their features using the empirical mode decomposition (EMD). The novel EMD—AOA—DLSTM neural predictor modeled in this study overcomes the vanishing and exploding gradients of basic recurrent neural learning and is tested for its superiority on the EV charging dataset of Georgia Tech, Atlanta, USA. At the time of simulation, the best results of 97.14% prediction accuracy with a mean absolute error of 0.1083 and a root mean square error of 2.0628 × 10 −5 are attained. Furthermore, the mean absolute error was evaluated to be 0.1083 and the mean square error pertaining to 4.25516 × 10 −10 . The results prove the efficacy of the prediction metrics computed with the novel deep learning LSTM neural predictor for the considered dataset in comparison with the previous techniques from existing works.

Keywords: electric vehicle; charging demand; LSTM neural predictor; deep learning; arithmetic optimizer; empirical mode decomposition; sustainable transport development; prediction accuracy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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