Smart Electric Vehicle (EVs) Charging Network Management Using Bidirectional GRU - AM Approaches

Electric Vehicles (EVs) are now essential since electrifying transportation has shown to be a game-changer in raising the sustainable and eco-friendly platform in global industry. Integrating Electric Vehicle Charging System (EVCS) as a new entity into the power distribution system is one of the most important and challenging concerns. The development of an EVCS network infrastructure is a key step toward the broad adoption of EVs. In order to make informed judgments about transmission, distribution, energy allocation, and charging station placement, the control center or central aggregator must have an accurate forecast of occupancy, consumption, and energy or charging demand. Data analytics and other methods have made it possible to regularly get information from the EVCS for the purposes of archiving and processing all of the data collected. This proposed approach to presents a solution to the aforementioned problems with energy demand forecasting for EVCS networks. Three steps make up the proposed method: preprocessing, feature selection, and model performance evaluation. Preprocessing data via normalization, feature selection by K-Means, and ultimately model evaluation via K-means. The proposed model has superior results to the LSTM, GRU, and BIGRU - AM models.