Metasurface-Based Solar Absorption Prediction System Using Artificial Intelligence
نویسندگان
چکیده
Solar energy is a significant, environment-friendly source of renewable energy. The solar absorber transforms radiation into heat as an effective green source. Therefore, increasing its absorbing capacity can improve absorber’s effectiveness. This paper proposes tungsten tantalum alloy with silicon dioxide (WTa-SiO2) ceramic layer-based system two different metasurfaces to enhance absorptivity and boost the efficacy. absorbance also improved by adjusting resonator thickness material thickness, maximum visible light absorption achieved suggested filter design. Moreover, Golden Eagle Optimization (GE)-based deep AlexNet algorithm proposed for predicting parameter variation their effect on absorbance. optimization technique used increase effectiveness optimizing design parameters. features from WTa-SiO2 are extracted Principal Component-Autoencoder (PC-AE) method. Experimental results show that effectively predict reduced computational time. method demonstrates superior prediction performance efficiency 99.8% compared existing methods. Thus, metasurface-based be photovoltaic applications.
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ژورنال
عنوان ژورنال: Journal of Mathematics
سال: 2023
ISSN: ['2314-4785', '2314-4629']
DOI: https://doi.org/10.1155/2023/9489270