AI-Based Wireless Charging of Electric Vehicles using Solar Energy

Abstract

The document introduces an AI-driven solar energy optimization system aimed at enhancing the efficiency, reliability, and scalability of solar power production. The system incorporates sophisticated machine learning methodologies, such as reinforcement learning (RL) for adaptive energy allocation, long short-term memory (LSTM) networks for solar energy prediction, and predictive maintenance frameworks utilizing support vector machines (SVM) and random forests for fault identification. The suggested methodology is evaluated using simulations and empirical tests at a 50 kW solar farm, integrated with IoT sensors and cloud computing infrastructure. Key performance indicators, including prediction accuracy, energy consumption, fault detection precision, and computing efficiency, are assessed and contrasted with traditional optimization techniques. The findings indicate that the AI-driven system surpasses conventional approaches in several dimensions, including a 15-20% enhancement in energy efficiency, an 85% defect detection rate, and a 20% increase in processing speed. These results illustrate the capacity of AI to improve the optimization of solar energy systems, facilitating the development of more intelligent and efficient renewable energy solutions. Subsequent study will concentrate on augmenting the system to include other renewable energy sources and investigate decentralized AI models for enhanced scalability.