Optimization of Users EV Charging Data Using Convolutional Neural Network

Transportation is necessary for modern living, yet the conventional combustion engine is quickly going out of style. All electric vehicles are quickly replacing gasoline and diesel vehicles because they create less pollution. The environment is greatly improved by fully electric vehicles (EVs), which produce no exhaust pollutants. Using modelling and optimization, researchers have concentrated on building smart scheduling algorithms to control the demand for public charging. To develop better forecasts, consider aspects such as prior historical data, start time, departure time, charge time hours, weekday, platform, and location id. Previous research has used algorithms like SVM and XGBOOST, with session time and energy usage receiving SMAPE ratings of 9.9% and 11.6%, respectively. The classifier model in the suggested method which makes use of CNN sequential architecture achieves the best prediction performance as a consequence. We emphasize the importance of charging behaviour predictions in both forecasts relative to one another and demonstrate a notable advancement over earlier work on a different dataset. Using various lengths of training data, we assess the behaviour prediction performance for increasing charge duration levels and charging time slots in contrast to prior work. The performance of the proposed technique is verified using actual EV charging data, and a comparison with other machine learning algorithms shows that it generally has higher prediction accuracy across all resolutions.