SYSTEM FOR OPTIMIZING ELECTRIC VEHICLE CHARGING SCHEDULES AND IMPROVING ACCURACY OF PREDICTING GREENHOUSE GAS EMISSIONS OF ELECTRIC VEHICLE CHARGING BASED ON TRANSFER LEARNING AND DEEP REINFORCEMENT LEARNING

A system for optimizing electric vehicle (EV) charging schedules and improving the accuracy of predicting greenhouse gas emissions from EV charging by using transfer learning and DDPG includes a mixed-integer linear programming (MILP) optimization module, a reinforcement learning (RL) module, a climate prediction module, an EV charging capacity prediction module, a transfer learning (TL) module, and an integration and control module, so as to integrate transfer learning, MILP, and RL. Based on MILP solutions and through RL agent-operated dynamic scheduling decisions, the knowledge of predicting EV charging capacity is transformed into predictions of greenhouse gas reduction. Ultimately, the integration and control module maximizes system performance, including reducing charging-related greenhouse gas emissions and minimizing costs to meet charging demands, thereby optimizing the charging schedules and accurately predicting the greenhouse gas reduction at charging stations.