Koohfar, SaharWoldemariam, WubeshetKumar, Amit2023-02-102023-02-102023-01-22Sustainability 15 (3): 2105 (2023)https://hdl.handle.net/20.500.12588/1710Electric 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.Attribution 4.0 United Stateshttps://creativecommons.org/licenses/by/4.0/electric vehiclestime seriesmachine learningdeep learningARIMASARIMARNNLSTMtransformersArticle2023-02-10