USING ARTIFICIAL INTELLIGENCE TO CALCULATE SOLAR COLLECTOR PARAMETERS (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2025)
This study explores the use of Artificial Intelligence  to predict and optimize key parameters of solar collectors, such as thermal efficiency and heat transfer rate. Traditional analytical methods are often limited by nonlinear environmental factors, while AI techniques like Artificial Neural Networks  and Genetic Algorithms  offer more accurate and adaptive modeling. Results show that AI-based models provide high prediction accuracy and can effectively optimize collector performance under varying conditions, improving the overall efficiency of solar energy systems.
Keywords: Artificial Intelligence; Machine Learning; Solar Energy; Solar Collector; Parameter Estimation; Energy Efficiency; Thermal Performance; Neural Networks; Predictive Modeling; Optimization; Data-driven Analysis; Heat Transfer; Computational Modeling; Smart Energy Systems; Renewable Energy Systems.