Electric Vehicles Charging Station Load Forecasting Integration With Renewable Energy Using Novel Deep EfficientBiLSTMNet

Abstract

The exponential increase in the adoption of Electric Vehicles (EVs) presents significant problems to the stability of the power grid. Therefore, it is crucial to accurately anticipate the demand for EV Charging Station (CS) to address this issue. To improve forecasts and identify CS load variables, existing studies are based on load profiling, which may be difficult to obtain for commercial EV charging stations. This paper proposes an efficient deep BiLSTMNet model to solve and mitigate these problems. Energy consumption and storage at four charging stations in California are analyzed. To guarantee accuracy and uniformity, the data is preprocessed by addressing missing values and ensuring consistency. A hybrid feature selection technique integrates the Boruta algorithm and SHAP (SHapley Additive exPlanations) values to ensure robust feature selection. The EfficientBiLSTMNet model, which integrates the EfficientNet and BiLSTM layers, is trained on the preprocessed datasets. The model's hyperparameters are optimized using an Enhanced Firefly Algorithm (EFA). The model performs a time series analysis to identify daily, weekly, monthly, and seasonal patterns in EV charging demand. The integration of renewable energy sources—specifically solar and wind generation—into the EV charging infrastructure is thoroughly examined in this study, not merely as input features but as key factors influencing the stability of charging demand at various stations. Their temporal patterns and environmental dependencies are leveraged to enhance forecasting accuracy and ensure grid-aware demand management across charging stations. The proposed model's performance is evaluated using metrics such as R-squared, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Simulation results demonstrate the effectiveness of the proposed model, with an average R-squared value of 0.9, MAE of 2.15 kW, and RMSE of 2.75 kW across the four stations. The EfficientBiLSTMNet model shows superior predictive accuracy compared to traditional models, highlighting the importance of comprehensive feature selection and engineering in forecasting EV charging demand. This study provides a robust framework for predicting EV charging demand, integrating renewable energy sources to enhance the stability and sustainability of the power grid amidst the increasing penetration of EVs.