Comprehensive Review of Electric Vehicle Energy Demand Forecasting Techniques

This study tackles a significant problem in predicting the demand for electric vehicle charging in order to design charging infrastructure, manage energy efficiently, and maintain grid stability. A novel system that focuses on important temporal patterns and features is created by fusing LSTM networks with attention processes to improve predictive performance. Effective preprocessing ensures that the incoming data is of good quality, which helps the LSTMs identify temporal correlations and the attention mechanism to highlight important information. It outperforms conventional methods like ARIMA and linear regression by using metrics like Mean Absolute Error (MAE) and R-squared (R2). A scalable and precise real-time energy management solution for sustainable transportation infrastructure is offered by this hybrid approach.