AI-Driven Energy Forecasting for Electric Vehicle Charging Stations Powered by Solar and Wind Energy

With the increasing demand for electric vehicles (EVs), accurate energy forecasting for charging stations powered by renewable sources is crucial. This study explores the implementation of an artificial intelligence (AI)-driven forecasting model for EV charging stations utilizing solar and wind energy. The model's precision is validated through comprehensive performance metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared (R2), showcasing its exceptional ability to predict energy production. Comparisons with traditional forecasting methods, such as ARIMA and Naive forecasting, highlight the evident superiority of the AI-driven model. A significantly higher R-squared value of 0.92 underscores its advanced capacity to navigate intricate relationships within renewable energy sources. Operational optimization, depicted through aligned predicted and actual energy consumption, illustrates the model's effectiveness in reducing reliance on grid electricity during peak hours, contributing to enhanced grid stability. The model's integration with renewable energy aligns with sustainability objectives, mitigating the carbon footprint associated with EV charging. The model's scalability and adaptability render it suitable for diverse geographic locations and varying energy generation capacities. The implications for sustainable mobility are substantial, as the model aligns with broader environmental goals by minimizing emissions linked to EV usage. In conclusion, this research emphasizes the transformative potential of AI-driven energy forecasting for EV charging infrastructure. The demonstrated precision, superiority over traditional models, and positive operational impact position the model as a crucial tool in advancing the transition toward sustainable and efficient transportation systems. Future research avenues may explore real-time adaptation and deeper integration with smart grid technologies to enhance the model's applicability in real-world scenarios.