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<p>High operating temperatures significantly reduce photovoltaic (PV) system efficiency, lowering power output by up to 20%. This review examines passive, active, and hybrid PV cooling techniques addressing heat management challenges. Passive methods such as radiative cooling and phase change materials reduce PV temperature by up to 20&#xa0;°C, improving electrical efficiency by 15.5%. Active cooling, including water and air systems, achieves larger temperature drops of up to 55&#xa0;°C and electrical efficiency gains of up to 22.2%, albeit with higher operational costs. Hybrid approaches combine passive and active methods to optimize performance, balancing complexity, and energy savings. Artificial intelligence (AI)-based control techniques, including Reinforcement Learning, Long Short-Term Memory networks, and Genetic Algorithms, enable real-time optimization by adjusting cooling parameters based on environmental data. These AI methods, validated through experimental and simulation studies, have realized up to 6.7% energy savings and improved system durability by mitigating thermal stress. AI further supports predictive maintenance and adaptive cooling strategy enhancement, advancing smart PV cooling solutions. Despite these advances, challenges related to cost, scalability, and environmental impact persist. This review compares the performance and trade-offs of existing cooling technologies, identifies research gaps, and underscores the potential of integrated AI-driven hybrid systems. These insights contribute to developing next-generation, efficient, and sustainable PV cooling technologies.</p> Graphical abstract <p></p>

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Multi-method cooling strategies for photovoltaic systems: a comprehensive review of passive, active, and AI-optimized hybrid techniques

  • 期刊论文

  • 发表日期：

    2025年7月2日

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  • Kehinde Temitope Alao,
  • Syed Ihtsham Ul Haq Gilani,
  • Kamaruzzaman Sopian,
  • Taiwo Onaopemipo Alao,
  • Zeshan Aslam,
  • Abdulhammed K. Hamzat,
  • Oluwaseun Ruth Alara

摘要

High operating temperatures significantly reduce photovoltaic (PV) system efficiency, lowering power output by up to 20%. This review examines passive, active, and hybrid PV cooling techniques addressing heat management challenges. Passive methods such as radiative cooling and phase change materials reduce PV temperature by up to 20 °C, improving electrical efficiency by 15.5%. Active cooling, including water and air systems, achieves larger temperature drops of up to 55 °C and electrical efficiency gains of up to 22.2%, albeit with higher operational costs. Hybrid approaches combine passive and active methods to optimize performance, balancing complexity, and energy savings. Artificial intelligence (AI)-based control techniques, including Reinforcement Learning, Long Short-Term Memory networks, and Genetic Algorithms, enable real-time optimization by adjusting cooling parameters based on environmental data. These AI methods, validated through experimental and simulation studies, have realized up to 6.7% energy savings and improved system durability by mitigating thermal stress. AI further supports predictive maintenance and adaptive cooling strategy enhancement, advancing smart PV cooling solutions. Despite these advances, challenges related to cost, scalability, and environmental impact persist. This review compares the performance and trade-offs of existing cooling technologies, identifies research gaps, and underscores the potential of integrated AI-driven hybrid systems. These insights contribute to developing next-generation, efficient, and sustainable PV cooling technologies.

Graphical abstract