AI in Machine Learning-Based Optimization of Solar-Powered EV Charging Networks Using IoT: A Comprehensive Framework

The chapter investigates how AI and machine learning algorithms strengthen solar-powered EV charging networks by improving their performance while enhancing sustainability aspects. The IoT sensors let the system perform live monitoring of systems and predict load patterns while dynamically distributing energy through its system. Solar power forecasting alongside LSTM-based power forecasting and reinforcement learning power optimization creates a hybrid framework that provides both efficiency along with flexibility. IoT sensors measure temperature as well as power flow combined with solar irradiance data and station occupancy figures to allow systems to make flexible operational adjustments. Finally researchers must continue improving artificial intelligence learning strategies for smart charging while cutting costs and enhancing network capabilities to ensure worldwide implementation.