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 language | English (US) |
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Title of host publication | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728193878 |
DOIs | |
State | Published - 2022 |
Externally published | Yes |
Event | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 - Detroit, United States Duration: Oct 9 2022 → Oct 13 2022 |
Publication series
Name | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
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Conference
Conference | 2022 IEEE Energy Conversion Congress and Exposition, ECCE 2022 |
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Country/Territory | United States |
City | Detroit |
Period | 10/9/22 → 10/13/22 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- charging load
- deep learning
- electric vehicles