Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence

Solar energy is a significant, environment-friendly source of renewable energy. The solar absorber transforms solar radiation into heat energy as an effective green energy source. Therefore, increasing its absorbing capacity can improve a solar absorber's effectiveness. This paper proposes a tungsten tantalum alloy with silicon dioxide (WTa-SiO2) ceramic layer-based solar absorber system with two different metasurfaces to enhance absorptivity and boost the solar absorber efficacy. The absorbance is also improved by adjusting the resonator thickness and material thickness, and the maximum visible light absorption is achieved by the suggested solar filter design. Moreover, Golden Eagle Optimization (GE)-based deep AlexNet algorithm is proposed for predicting the parameter variation and their effect on absorbance. The optimization technique is used to increase the effectiveness of the solar absorber by optimizing the design parameters. The features from the WTa-SiO2 design are extracted by the proposed Principal Component-Autoencoder (PC-AE) method. Experimental results show that the proposed system can effectively predict absorptivity with a reduced computational time. The proposed method demonstrates superior prediction performance with an absorption prediction efficiency of 99.8% compared to the existing methods. Thus, the proposed WTa-SiO2 metasurface-based solar absorber can be used for photovoltaic applications.