MODELING SOLAR COLLECTORS: A REVIEW OF MODERN METHODS TO IMPROVE ENERGY CONVERSION EFFICIENCY UNDER CHANGING CLIMATIC CONDITIONS
DOI:
https://doi.org/10.11113/jest.v7.181Keywords:
Solar collectors, LSTM, Neural networks, Computational Fluid Dynamics, Solar tracking systemsAbstract
Efficiency and reliability in solar energy systems are largely influenced by the development of solar collector technologies and methodologies adopted for performance modeling under variable climatic conditions. This paper reviews recent approaches to modeling solar collectors, with a primary emphasis on the integration of solar tracking systems with advanced computational techniques such as artificial intelligence and hybrid models. The review highlights the use of long short-term memory (LSTM) models, neural networks, and AI-driven optimizations for enhancing energy conversion efficiency. It also provides an overview of the contributions of computational fluid dynamics (CFD) and thermodynamic models to the structural and thermal performance of solar collectors, mainly parabolic trough collectors (PTCs) and systems utilizing nanofluids as working mediums. Various case studies demonstrate the significant impacts of these modeling methods on improving efficiency and adapting to real-time environmental changes. The review concludes with recommendations for future work in integrating artificial intelligence (AI), hybrid model development, new material explorations, and long-term field studies to refine and enhance solar collector performance. These advancements are expected to contribute significantly to the future of solar energy, fostering sustainable and efficient energy solutions.
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