Forecasting Long-term Electric Vehicle Energy Demand in a Specific Geographic Region

McBee, Kerry D.; Bukofzer, D.; Chong, John; Bhullar, Sandeep Singh

doi:10.1109/pesgm41954.2020.9282045

Cited by 5 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Saputra et al (2019) proposed novel approaches using state-of-the-art machine learning techniques, aiming at predicting energy demand for electric vehicles [10]. McBee et al ( 2020) developed a long-term forecasting approach by combining all attributes required to predict energy demand of EV penetration [11]. Zhang et al (2020) presented a prediction-based optimal energy management of electric vehicles usinganextreme learning machine algorithm and also to provide the driver torque demand prediction [12].…”

Section: Related Work and Motivationsmentioning

confidence: 99%

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

Shanmuganathan

Victoire

Balraj³

et al. 2022

Sustainability

View full text Add to dashboard Cite

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.

show abstract

Section: Related Work and Motivationsmentioning

confidence: 99%

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

Shanmuganathan

Victoire

Balraj³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

Vehicle Forecasting: A Machine Learning Approach for Predicting and Preventing Vehicle Issues, Based on the Design Thinking Approach

et al. 2024

2024 International Conference on Expert Clouds and Applications (ICOECA)

View full text Add to dashboard Cite

Optimal Centralized Charging Station With Demand Response System for Commercial EVs

Ávila

Mandal

2022

2022 North American Power Symposium (NAPS)

View full text Add to dashboard Cite

Forecasting Long-term Electric Vehicle Energy Demand in a Specific Geographic Region

Cited by 5 publications

References 7 publications

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

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

Vehicle Forecasting: A Machine Learning Approach for Predicting and Preventing Vehicle Issues, Based on the Design Thinking Approach

Optimal Centralized Charging Station With Demand Response System for Commercial EVs

Contact Info

Product

Resources

About