Advanced Mathematical Modeling and Artificial Intelligence Frameworks for the Thermal Management of Photovoltaic Systems: A Theoretical Review

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

The integration of solar photovoltaic (PV) systems into the global energy grid is limited by thermal-induced efficiency degradation, where cell performance typically drops by 0.3% to 0.5% for every degree Celsius rise above standard conditions. While hardware-based cooling methods are well-documented, the optimization of these systems increasingly relies on sophisticated mathematical modeling and Artificial Intelligence (AI) to manage complex heat transfer dynamics. This review paper provides a comprehensive analysis of the mathematical and computational paradigms used for the thermal control of PV modules. We explore the evolution from 1D steady-state energy balance equations to multi-dimensional Computational Fluid Dynamics (CFD) and high-fidelity Physics-Informed Neural Networks (PINNs). Furthermore, the role of intelligent control algorithms, including Reinforcement Learning (RL) for active pump regulation and Fuzzy Logic Control (FLC) for adaptive thermal absorbers, is critically assessed. The review finds that while traditional deterministic models offer physical interpretability, AI-driven architectures—specifically those incorporating transfer learning and real-time optimization—achieve superior predictive accuracy (up to 99%) and operational efficiency gains of 10% to 15% in variable climatic environments.

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