Deep Learning Tackles Temporal Predictions on Charging Loads of Electric Vehicles

Eugenia Cadete, Raul Alva, Albert Zhang, Caiwen Ding, Mimi Xie, Sara Ahmed, Yufang Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

With the prediction of 145 million electric vehicles on the road by 2030, accommodation of charging needs for these electric vehicles will impose extra challenges to power grid strength. It is imperative to predict charging loads for future infrastructure improvement, including new charging stations' installation to meet the electric vehicles' charging needs and reduce the power grid overload. In this study, deep learning approaches including Artificial Neural Networks, Recursive Neural Networks, and Long-Short Term Memory models are used to predict the charging load with daily and weekly patterns using public datasets. The performances of the deep learning models were compared against the auto-regressive moving average model concerning convergence speed, MSE, RMSE, MAE, and R-squared. The long-short term memory model outperformed all other models concerning the evaluation metrics.

Original languageEnglish (US)
Title of host publication2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728193878
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 - Detroit, United States
Duration: Oct 9 2022Oct 13 2022

Publication series

Name2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022

Conference

Conference2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022
Country/TerritoryUnited States
CityDetroit
Period10/9/2210/13/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

  • charging load
  • deep learning
  • electric vehicles

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