Performance enhancing solar energy absorber with structure optimization and absorption prediction with KNN regressor model

被引：6

作者：

Armghan, Ammar ^{[1
]}

Htay, Mya Mya ^{[2
]}

Alsharari, Meshari ^{[1
]}

Aliqab, Khaled ^{[1
]}

Surve, Jaymit ^{[3
]}

Patel, Shobhit K. ^{[4
]}

机构：

[1] Jouf Univ, Coll Engn, Dept Elect Engn, Sakaka 72388, Saudi Arabia

[2] Marwadi Univ, Dept Informat & Commun Technol, Rajkot 360003, Gujarat, India

[3] Marwadi Univ, Dept Elect Engn, Rajkot 360003, Gujarat, India

[4] Marwadi Univ, Dept Comp Engn, Rajkot 360003, Gujarat, India

来源：

ALEXANDRIA ENGINEERING JOURNAL | 2023年 / 82卷

关键词：

KNN Regressor; Solar Energy; Absorption; Solar spectrum; Machine Learning; Optimization; FUTURE;

D O I：

10.1016/j.aej.2023.10.017

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

We used the honeycomb resonator structure and explored its absorptance response in the visible, ultraviolet, to mid-infrared regions. In this solar spectrum range, the average absorption is greater than 90%. In addition, the spectral absorption is 97.69%, 96.35%, 94.45%, and 96.11% in the respective solar spectrum regions, respectively. In this work, we observed the highest absorption response of the solar spectrum. Further, the KNN regressor model is employed for behavior prediction of the proposed absorber structure and results indicate this model highly predicts the absorption values for lower K values. This proposed model due to its ideal characteristics can be employed for a vast range of applications including performance improvement of solar cells, thermophotovoltaics, and many more.

引用

页码：531 / 540

页数：10

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