USING ARTIFICIAL INTELLIGENCE TO CALCULATE SOLAR COLLECTOR PARAMETERS

Ibrokhimov Abdulfatto Raximjon ugli; Isakov Botirjon Umarovich; Muminov Bahodir Boltayevich

Authors

Ibrokhimov Abdulfatto Raximjon ugli Belarus–Uzbekistan Intersectoral Institute of Applied Technical Qualifications Tashkent
Isakov Botirjon Umarovich Belarus–Uzbekistan Intersectoral Institute of Applied Technical Qualifications Tashkent
Muminov Bahodir Boltayevich Tashkent State University of Economics

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.

Abstract

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.

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