Prediction of Electric Vehicles Charging Load Using Long Short-Term Memory Model

Cadete, Eugenia; Ding, Caiwen; Xie, Mimi; Ahmed, Sara; Jin, Yufang

doi:10.1061/9780784483787.006

Cited by 2 publications

(6 citation statements)

References 12 publications

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“…Shahriar et al (2021) proposed the usage of historical charging data in conjunction with weather, traffic, and events data to predict EV session duration and energy consumption using popular machine learning algorithms [23]. Cadete et al (2021) studied long short-term memory and autoregressive and moving average models to predict charging loads with temporal profiles from three EV charging stations [24]. modeled a driving condition prediction model based on a BP neural network for parallel hybrid electric vehicles [25].…”

Section: Related Work and Motivationsmentioning

confidence: 99%

“…Table 8 provides a comparative analysis of the developed EMD-AOA based deep LSTM predictor with that of the earlier prediction techniques from existing works [1,10,13,16,22,24,27,50,52]. The same Georgia Tech EV datasets were presented as input to all the comparison models for the respective codes in github.com and their comparison metrics-MSE, training efficiency, testing efficiency, computational time and prediction accuracy was evaluated.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Any model which has relatively high correlation and low RMSE will be marked as OBS in the X-axis of the Taylor diagram. The RMSE calculated using Equation (24) and the correlation value are used to arrive at the observed (OBS) value. The standard deviation of the test data can be related to the radial distance from the origin of the plot.…”

Section: Comparative Analysismentioning

confidence: 99%

“…using Equation (24) and the correlation value are used to arrive at the observed (OBS) value. The standard deviation of the test data can be related to the radial distance from the origin of the plot.…”

Section: Comparative Analysismentioning

confidence: 99%

See 3 more Smart Citations

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

Shanmuganathan

Victoire

Balraj³

et al. 2022

Sustainability

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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.

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Section: Related Work and Motivationsmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

See 2 more Smart Citations

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

Shanmuganathan

Victoire

Balraj³

et al. 2022

Sustainability

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show abstract

“…However, postprocessing was needed to handle constraints in the data. Meanwhile, the study [21] found that LSTM networks outperformed autoregressive integrated moving average (ARIMA) models for predicting charging loads. The drawback of that study was the lack of data during holidays and semester breaks.…”

Section: Introductionmentioning

confidence: 99%

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

Erdogan,

Fekih Hassen

2023

Energies

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The growing demand for electric vehicles (EV) in the last decade and the most recent European Commission regulation to only allow EV on the road from 2035 involved the necessity to design a cost-effective and sustainable EV charging station (CS). A crucial challenge for charging stations arises from matching fluctuating power supplies and meeting peak load demand. The overall objective of this paper is to optimize the charging scheduling of a hybrid energy storage system (HESS) for EV charging stations while maximizing PV power usage and reducing grid energy costs. This goal is achieved by forecasting the PV power and the load demand using different deep learning (DL) algorithms such as the recurrent neural network (RNN) and long short-term memory (LSTM). Then, the predicted data are adopted to design a scheduling algorithm that determines the optimal charging time slots for the HESS. The findings demonstrate the efficiency of the proposed approach, showcasing a root-mean-square error (RMSE) of 5.78% for real-time PV power forecasting and 9.70% for real-time load demand forecasting. Moreover, the proposed scheduling algorithm reduces the total grid energy cost by 12.13%.

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Prediction of Electric Vehicles Charging Load Using Long Short-Term Memory Model

Cited by 2 publications

References 12 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

Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations

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