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    Adaptive Neural Architecture Search Using Meta-Heuristics: Discovering Fine-Tuned Predictive Models for Photocatalytic CO2 Reduction
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024) Işıkdağ, Ümit; Bekdaş, Gebrail; Aydın, Yaren; Apak, Sudi; Hong, Junhee; Geem, Zong Woo
    This study aims to contribute to the reduction of carbon dioxide and the production of hydrogen through an investigation of the photocatalytic reaction process. Machine learning algorithms can be used to predict the hydrogen yield in the photocatalytic carbon dioxide reduction process. Although regression-based approaches provide good results, the accuracy achieved with classification algorithms is not very high. In this context, this study presents a new method, Adaptive Neural Architecture Search (NAS) using metaheuristics, to improve the capacity of ANNs in estimating the hydrogen yield in the photocatalytic carbon dioxide reduction process through classification. The NAS process was carried out with a tool named HyperNetExplorer, which was developed with the aim of finding the ANN architecture providing the best prediction accuracy through changing ANN hyperparameters, such as the number of layers, number of neurons in each layer, and the activation functions of each layer. The nature of the NAS process in this study was adaptive, since the process was accomplished through optimization algorithms. The ANNs discovered with HyperNetExplorer demonstrated significantly higher prediction performance than the classical ML algorithms. The results indicated that the NAS helped to achieve better performance in the estimation of the hydrogen yield in the photocatalytic carbon dioxide reduction process. © 2024 by the authors.
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    Öğe
    An Investigation into the Ability of a Solar Photovoltaic-Hydrogen System to Meet the Electrical Energy Demand of Houses in Different Cities in Türkiye
    (MDPI, 2025) Özçelep, Yasin; Bekdaş, Gebrail; Apak, Sudi; Geem, Zong Woo
    In this study, the annual electricity consumption of nine real houses from different cities in T & uuml;rkiye was recorded on a monthly basis. The feasibility of meeting the electrical energy needs of houses with hydrogen and supplying the energy required for hydrogen production using solar panels is examined. The annual electricity consumption of the houses was normalized based on house size. The solar panel area for hydrogen production needed for these houses was defined. Additionally, it was calculated that the average volumetric amount of hydrogen produced per hour during peak sun hours in the investigated cities was 1 m3/h. This approach reduced the solar panel area for hydrogen production by a factor of 1.7.