Efficiency-aware machine-learning driven design of solar harvester for renewable energy application
Although MIM structures have been studied for solar thermal applications for decades, the multilayer MI-MIM structure is the subject of this study's investigation for batter absorption. In order to identify an effective solar absorber, two configurations Gold-MgF2-Gold (GMGSA) and Gold-MgF2-Gol...
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024013057 |
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| author | Abdullah Baz Shobhit K. Patel |
| author_facet | Abdullah Baz Shobhit K. Patel |
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| description | Although MIM structures have been studied for solar thermal applications for decades, the multilayer MI-MIM structure is the subject of this study's investigation for batter absorption. In order to identify an effective solar absorber, two configurations Gold-MgF2-Gold (GMGSA) and Gold-MgF2-Gold-MgF2-Gold (GMGMGSA) are examined in this work. By using machine learning approaches, this research was able to get an MSE of 2.8091 × 10−5 and an R2 value of 0.998836. The optical response and parameter optimization analysis have been examined in this work in the wavelength range of 0.4 μm–1.6 μm. When exposed to AM 1.5 spectrum radiation, the GMGMGSA absorbs 93.10 % of the radiation, compared to 88.30 % for the single-layer GMGSA. Furthermore, both structures show steady absorption that is independent of polarization up to a 70° incidence angle. The aforementioned discoveries render the structure a plausible contender for solar thermal uses, including battery-integrated photovoltaic (BIPV), commercial solar power (CSP), and solar water heating. This might lead to a future where energy is more sustainably produced. |
| format | Article |
| id | doaj-art-8cdd0bf97e864024bcab7baa41797678 |
| institution | OA Journals |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
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| series | Results in Engineering |
| spelling | doaj-art-8cdd0bf97e864024bcab7baa417976782025-08-20T01:58:30ZengElsevierResults in Engineering2590-12302024-12-012410305010.1016/j.rineng.2024.103050Efficiency-aware machine-learning driven design of solar harvester for renewable energy applicationAbdullah Baz0Shobhit K. Patel1Department of Computer and Network Engineering, College of Computing, Umm Al-Qura University, Makkah, Saudi Arabia; Corresponding author.Department of Computer Engineering, Marwadi University, Rajkot, Gujarat, 360003, India; Corresponding author.Although MIM structures have been studied for solar thermal applications for decades, the multilayer MI-MIM structure is the subject of this study's investigation for batter absorption. In order to identify an effective solar absorber, two configurations Gold-MgF2-Gold (GMGSA) and Gold-MgF2-Gold-MgF2-Gold (GMGMGSA) are examined in this work. By using machine learning approaches, this research was able to get an MSE of 2.8091 × 10−5 and an R2 value of 0.998836. The optical response and parameter optimization analysis have been examined in this work in the wavelength range of 0.4 μm–1.6 μm. When exposed to AM 1.5 spectrum radiation, the GMGMGSA absorbs 93.10 % of the radiation, compared to 88.30 % for the single-layer GMGSA. Furthermore, both structures show steady absorption that is independent of polarization up to a 70° incidence angle. The aforementioned discoveries render the structure a plausible contender for solar thermal uses, including battery-integrated photovoltaic (BIPV), commercial solar power (CSP), and solar water heating. This might lead to a future where energy is more sustainably produced.http://www.sciencedirect.com/science/article/pii/S2590123024013057Machine learningRenewable energyPhotothermal energySolar thermal collectorCSP |
| spellingShingle | Abdullah Baz Shobhit K. Patel Efficiency-aware machine-learning driven design of solar harvester for renewable energy application Results in Engineering Machine learning Renewable energy Photothermal energy Solar thermal collector CSP |
| title | Efficiency-aware machine-learning driven design of solar harvester for renewable energy application |
| title_full | Efficiency-aware machine-learning driven design of solar harvester for renewable energy application |
| title_fullStr | Efficiency-aware machine-learning driven design of solar harvester for renewable energy application |
| title_full_unstemmed | Efficiency-aware machine-learning driven design of solar harvester for renewable energy application |
| title_short | Efficiency-aware machine-learning driven design of solar harvester for renewable energy application |
| title_sort | efficiency aware machine learning driven design of solar harvester for renewable energy application |
| topic | Machine learning Renewable energy Photothermal energy Solar thermal collector CSP |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024013057 |
| work_keys_str_mv | AT abdullahbaz efficiencyawaremachinelearningdrivendesignofsolarharvesterforrenewableenergyapplication AT shobhitkpatel efficiencyawaremachinelearningdrivendesignofsolarharvesterforrenewableenergyapplication |