Hybrid ARBS-Net Framework for Accurate Energy Forecasting in Smart Grid-Driven Electric Mobility Environments

The main objective of this study are to develop an intelligent forecasting for Electric Vehicle Charging Station (EVCS) and to significantly enhance the accuracy of energy consumption forecasting in renewable integrated smart grid environments. These objectives are achieved through solving the following tasks: implementing data preprocessing to handle missing values, remove outliers and eliminate inconsistent observations for improving dataset reliability; performing feature engineering for generating meaningful temporal and derived variables that strengthen model interpretability; and carrying out detailed Exploratory Data Analysis (EDA) for extracting statistical trends, recognize correlations and uncover hidden temporal dependencies in energy consuption behaviour. Structure on the preparatory stages, a hybrid Deep Learning (DL) approach using a Radial Basis Spiking Net (ARBSNet) is developed by combining radial basis kernal (RBF) with temporal behavior of Spiking Neural Networks (SNN), enhanced with attention mechanisms for capturing non-linear fluctuations and time varying required pattern. The most important results obtained from Python based experiments highlight enhancement in forcasting performance, with the proposed model achieving a Mean Squared Error (MSE) of 0.1183, a Mean Absolute Error (MAE) of 0.2694, a Root Mean Squared Error (RMSE) of 0.3439, and an overall prediction accuracy reaching a 2 R score of 0.99. The significant of the results lies in their ability to support predictive energy allocation, optimize load balancing strategies and improve grid stability. By providing highly dependable demand forecasts for charging infrastructure, the proposed framework contributes to the sustainable integration of electric mobility within future smart energy systems.