Prediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach

dc.contributor.authorKoohfar, Sahar
dc.contributor.authorWoldemariam, Wubeshet
dc.contributor.authorKumar, Amit
dc.date.accessioned2023-02-10T14:28:52Z
dc.date.available2023-02-10T14:28:52Z
dc.date.issued2023-01-22
dc.date.updated2023-02-10T14:28:53Z
dc.description.abstractElectric vehicles have been gaining attention as a cleaner means of transportation that is low-carbon and environmentally friendly and can reduce greenhouse gas emissions and air pollution. Despite EVs' many advantages, widespread adoption will negatively affect the electric grid due to their random and volatile nature. Consequently, predicting the charging demand for electric vehicles is becoming a priority to maintain a steady supply of electric energy. Time series methodologies are applied to predict the charging demand: traditional and deep learning. RNN, LSTM, and transformers represent deep learning approaches, while ARIMA and SARIMA are traditional techniques. This research represents one of the first attempts to use the Transformer model for predicting EV charging demand. Predictions for 3-time steps are considered: 7 days, 30 days, and 90 days to address both short-term and long-term forecasting of EV charging load. RMSE and MAE were used to compare the model's performance. According to the results, the Transformer outperforms the other mentioned models in terms of short-term and long-term predictions, demonstrating its ability to address time series problems, especially EV charging predictions. The proposed Transformers framework and the obtained results can be used to manage electricity grids efficiently and smoothly.
dc.description.departmentCivil and Environmental Engineering, and Construction Management
dc.identifierdoi: 10.3390/su15032105
dc.identifier.citationSustainability 15 (3): 2105 (2023)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1710
dc.rightsAttribution 4.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectelectric vehicles
dc.subjecttime series
dc.subjectmachine learning
dc.subjectdeep learning
dc.subjectARIMA
dc.subjectSARIMA
dc.subjectRNN
dc.subjectLSTM
dc.subjecttransformers
dc.titlePrediction of Electric Vehicles Charging Demand: A Transformer-Based Deep Learning Approach
dc.typeArticle

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