Prediction of electric vehicles charging load using long short-term memory model

Eugenia Cadete; Caiwen Ding; Mimi Xie; Sara Ahmed; Yu Fang Jin

doi:10.1061/9780784483787.006

Prediction of electric vehicles charging load using long short-term memory model

Eugenia Cadete, Caiwen Ding, Mimi Xie, Sara Ahmed, Yu Fang Jin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

The number of electric vehicles (EV) has increased significantly in the past decades due to its advantages including emission reduction and improved energy efficiency. However, the adoption of EV could lead to overloading the grid and degrading the power quality of the distribution system. It also demands an increase in the number of EV charging stations. To meet the charging needs of 15 million EVs by the year 2030 with limited charging stations, prediction of charging needs, and reallocating charging resources are in emerging needs. In this study, long short-term memory (LSTM) and autoregressive and moving average models (ARMA) models were applied to predict charging loads with temporal profiles from 3 charging stations. Prediction accuracy was applied to evaluate the performance of the models. The LSTM models demonstrated a significant performance improvement compared to ARMA models. The results from this study lay a foundation to efficiently manage charge resources.

Original language	English (US)
Title of host publication	Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021
Editors	Zahid Hossain, Marwa Hassan, Louay N. Mohammad
Publisher	American Society of Civil Engineers (ASCE)
Pages	52-58
Number of pages	7
ISBN (Electronic)	9780784483787
DOIs	https://doi.org/10.1061/9780784483787.006
State	Published - 2021
Externally published	Yes
Event	Tran-SET Conference 2021 - Virtual, Online Duration: Jun 3 2021 → Jun 4 2021

Publication series

Name	Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021

Conference

Conference	Tran-SET Conference 2021
City	Virtual, Online
Period	6/3/21 → 6/4/21

Bibliographical note

Publisher Copyright:
© 2021 American Society of Civil Engineers.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1061/9780784483787.006

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Cadete, E., Ding, C., Xie, M., Ahmed, S., & Jin, Y. F. (2021). Prediction of electric vehicles charging load using long short-term memory model. In Z. Hossain, M. Hassan, & L. N. Mohammad (Eds.), Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021 (pp. 52-58). (Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784483787.006

Prediction of electric vehicles charging load using long short-term memory model. / Cadete, Eugenia; Ding, Caiwen; Xie, Mimi et al.
Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021. ed. / Zahid Hossain; Marwa Hassan; Louay N. Mohammad. American Society of Civil Engineers (ASCE), 2021. p. 52-58 (Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cadete, E, Ding, C, Xie, M, Ahmed, S & Jin, YF 2021, Prediction of electric vehicles charging load using long short-term memory model. in Z Hossain, M Hassan & LN Mohammad (eds), Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021. Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021, American Society of Civil Engineers (ASCE), pp. 52-58, Tran-SET Conference 2021, Virtual, Online, 6/3/21. https://doi.org/10.1061/9780784483787.006

Cadete E, Ding C, Xie M, Ahmed S, Jin YF. Prediction of electric vehicles charging load using long short-term memory model. In Hossain Z, Hassan M, Mohammad LN, editors, Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021. American Society of Civil Engineers (ASCE). 2021. p. 52-58. (Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021). doi: 10.1061/9780784483787.006

Cadete, Eugenia ; Ding, Caiwen ; Xie, Mimi et al. / Prediction of electric vehicles charging load using long short-term memory model. Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021. editor / Zahid Hossain ; Marwa Hassan ; Louay N. Mohammad. American Society of Civil Engineers (ASCE), 2021. pp. 52-58 (Tran-SET 2021 - Proceedings of the Tran-SET Conference 2021).

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abstract = "The number of electric vehicles (EV) has increased significantly in the past decades due to its advantages including emission reduction and improved energy efficiency. However, the adoption of EV could lead to overloading the grid and degrading the power quality of the distribution system. It also demands an increase in the number of EV charging stations. To meet the charging needs of 15 million EVs by the year 2030 with limited charging stations, prediction of charging needs, and reallocating charging resources are in emerging needs. In this study, long short-term memory (LSTM) and autoregressive and moving average models (ARMA) models were applied to predict charging loads with temporal profiles from 3 charging stations. Prediction accuracy was applied to evaluate the performance of the models. The LSTM models demonstrated a significant performance improvement compared to ARMA models. The results from this study lay a foundation to efficiently manage charge resources.",

author = "Eugenia Cadete and Caiwen Ding and Mimi Xie and Sara Ahmed and Jin, {Yu Fang}",

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AB - The number of electric vehicles (EV) has increased significantly in the past decades due to its advantages including emission reduction and improved energy efficiency. However, the adoption of EV could lead to overloading the grid and degrading the power quality of the distribution system. It also demands an increase in the number of EV charging stations. To meet the charging needs of 15 million EVs by the year 2030 with limited charging stations, prediction of charging needs, and reallocating charging resources are in emerging needs. In this study, long short-term memory (LSTM) and autoregressive and moving average models (ARMA) models were applied to predict charging loads with temporal profiles from 3 charging stations. Prediction accuracy was applied to evaluate the performance of the models. The LSTM models demonstrated a significant performance improvement compared to ARMA models. The results from this study lay a foundation to efficiently manage charge resources.

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Prediction of electric vehicles charging load using long short-term memory model

Abstract

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Bibliographical note

UN SDGs

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