AI-Driven Optimization of Cascaded Seawater-Cooled PV-CSP Hybrid Systems for Arid Coastal Environments: Coastal Saudi Arabia as a Model

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This study presents a novel, Artificial Intelligence (AI)-enhanced hybrid Photovoltaic (PV)-Concentrated Solar Power (CSP) system designed for arid coastal environments (using coastal Saudi Arabia as a model), which fundamentally transforms seawater-based PV cooling from a conventional operational expense into a productive thermal input for CSP generation, thereby increasing overall conversion efficiency by up to 30%. By employing a sophisticated random forest-based machine-learning model to optimize site selection, the research achieves over 90% classification accuracy and reduces analysis time by more than 50% compared to traditional manual methods, identifying Tabuk as a prime location. The suggested integrated, cascaded architecture eliminates reliance on freshwater resources while ensuring reliable, dispatchable power through advanced thermal energy storage and bidirectional power conversion, offering a scalable and sustainable solution that aligns with Saudi Vision 2030 energy objectives.

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