Data Clustering and Genetic Algorithm for the Design Optimization of a Hybrid Concentrated Solar System for SHIP
Abstract
Building solar thermal systems producing heat for industrial processes could contribute greatly to reducing global emissions. Such installations should be cost-effective and energy-efficient, which is why we investigated here a new algorithm for design optimization. A clustering algorithm of meteorological data is adapted for solar thermal energy production and a genetic algorithm is performed to find the optimal sizing. Two different control strategies are tested within the optimization algorithm to investigate the impact on the accuracy of the resulting solutions.