Computational Modeling and AI Optimization in Renewable Energy: Floating Solar Panels and Circular Economy Applications

Abstract

This study examines the integration of computational modeling, artificial intelligence (AI) optimization, and circular economy principles to enhance the efficiency and sustainability of floating solar panel systems. This research employs computational modeling with ANSYS Fluent to simulate the thermal and electrical performance of floating solar panels across various climatic conditions. Artificial intelligence optimization methods—machine learning (Gradient Boosting Regressor), evolutionary algorithms, reinforcement learning (Deep Q-Networks)—are used to maximize energy yield and adaptive performance. Outcomes show that floating solar panels have an 8.5% increase in energy yield and a 12.1% decrease in material degradation compared to ground installations. AI optimization resulted in a 7.2% improvement in energy output using genetic algorithms and a 5.6% improvement with real-time reinforcement learning adjustments. The study highlights the use of very recyclable materials using the principles of circular economy, resulting in a possible 25% reduction in waste and a 30% improvement in system lifespan. This integrated strategy shows that combining sustainable design with state-of-the-art computational methods enhances the environmental sustainability and operational performance of floating solar technology, catering to the twofold concerns of renewable power generation and green responsibility.