ABSTRACT Photovoltaic (PV) panels experience substantial efficiency losses under thermal stress, particularly in arid climates. This work introduces a novel segmented water‐cooling configuration, where the PV surface is divided into multiple independently cooled sections to enhance localized heat extraction. The segmentation concept is further optimized using artificial intelligence (AI) algorithms—Genetic Algorithm, Particle Swarm Optimization (PSO), and Artificial Bee Colony—to determine the most efficient combination of cooling parameters. Results show that increasing the number of cooling sections from one to seven raises the electrical efficiency from 14.85% to 17.44% (a 17.4% relative gain), while the average PV cell temperature decreases from 57.5°C to 50.1°C (−12.9%). AI optimization confirmed that the PSO algorithm achieves equivalent peak performance with only three cooling sections and a lower water flow rate (0.0843 kg/s), ensuring both thermal stability and water‐use efficiency. The proposed AI‐guided segmentation strategy enhances PV output and operational longevity while minimizing system complexity, offering a cost‐effective, scalable solution for solar installations in hot and arid environments.
Almetwall et al. (Tue,) studied this question.