PERTANIKA JOURNAL OF SCIENCE AND TECHNOLOGY

Revolutionizing solar energy utilization through solar pavement technology offers a path to sustainable infrastructure and reduced greenhouse emissions. This review article synthesizes findings from experimental, numerical and machine learning-based studies to optimize solar energy harvesting in pavement applications. The experimental and numerical analyses focus on achieving optimal thermal efficiency and maximizing the outlet pipe’s temperature in Pavement Solar Collectors (PSC) through detailed parametric studies. Machine learning tools are then employed to further enhance PSC performance by integrating additional input parameters, varied PSC designs, and diverse environmental conditions. Key insights from this review indicate that integrating machine learning into PSC design significantly broadens the scope and efficiency of these technologies, positioning solar pavement as a viable approach to reducing greenhouse gas emissions. Future works from this technology include looking into a multi-functional renewable energy system that produces hydrogen powered by solar power. Additionally, the adaptability of these developed models suggests potential applications in solar collectors for roof tiles, building walls, and related energy-efficient systems.

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