Optimization in the Geometric Design of Solar Collectors Using Generative AI Models (GANs)

This scientific article aims to develop a model based on generative artificial intelligence for the optimization of the geometric design of solar thermal collectors, with the goal of maximizing their thermal efficiency in residential and industrial applications. A methodological approach is followed, which employs generative neural networks to explore the design space of the collectors, producing a variety of novel and improved configurations. Through simulations, the performance of each design is evaluated in terms of efficiency, considering parameters such as collector area, geometric enhancements (e.g., reflectors or selective covers), and solar radiation conditions.The results demonstrate that the generative model is capable of increasing the thermal efficiency of collectors compared to baseline designs, identifying optimal configurations with efficiencies close to 75%. Simulated data graphs and tables are included, along with a flow diagram of the proposed process and pseudocode of the optimization algorithm.The literature review covers previous optimization techniques (heuristics, machine learning) and fundamentals of generative AI (GANs, transformers, diffusion models), justifying the choice of the technique used. The proposed generative approach proves to be a promising tool for the automated design of solar thermal collectors, capable of enhancing energy performance and providing innovative solutions in solar engineering.