Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks
In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, incl...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi
2024
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| Online Access: | https://hdl.handle.net/1721.1/153427 |
| _version_ | 1826214186785439744 |
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| author | Alghamdi, Hisham Maduabuchi, Chika Okoli, Kingsley Albaker, Abdullah Alatawi, Ibrahim Alsafran, Ahmed S. Alkhedher, Mohammad Chen, Wei-Hsin |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Alghamdi, Hisham Maduabuchi, Chika Okoli, Kingsley Albaker, Abdullah Alatawi, Ibrahim Alsafran, Ahmed S. Alkhedher, Mohammad Chen, Wei-Hsin |
| author_sort | Alghamdi, Hisham |
| collection | MIT |
| description | In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions. |
| first_indexed | 2024-09-23T16:01:29Z |
| format | Article |
| id | mit-1721.1/153427 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:01:29Z |
| publishDate | 2024 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | mit-1721.1/1534272024-07-12T16:34:30Z Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks Alghamdi, Hisham Maduabuchi, Chika Okoli, Kingsley Albaker, Abdullah Alatawi, Ibrahim Alsafran, Ahmed S. Alkhedher, Mohammad Chen, Wei-Hsin Massachusetts Institute of Technology. Department of Nuclear Science and Engineering In the relentless pursuit of sustainable energy solutions, this study pioneers an innovative approach to integrating thermoelectric generators (TEGs) and photovoltaic (PV) modules within hybrid systems. Uniquely, it employs neural networks for an exhaustive analysis of a plethora of parameters, including a diverse spectrum of semiconductor materials, cooling film coefficients, TE leg dimensions, ambient temperature, wind speed, and PV emissivity. Leveraging a rich dataset, the neural network is meticulously trained, revealing intricate interdependencies among parameters and their consequential impact on power generation and the efficiencies of TEG, PV, and integrated PV-TE systems. Notably, the hybrid system witnesses a striking 23.1% augmentation in power output, escalating from 0.26 W to 0.32 W, and a 20% ascent in efficiency, from 14.68% to 17.62%. This groundbreaking research illuminates the transformative potential of integrating TEGs and PV modules and the paramountcy of multifaceted parameter optimization. Moreover, it exemplifies the deployment of machine learning as a powerful tool for enhancing hybrid energy systems. This study, thus, stands as a beacon, heralding a new chapter in sustainable energy research and propelling further innovations in hybrid system design and optimization. Through its novel approach, it contributes indispensably to the arsenal of clean energy solutions. 2024-01-30T20:29:25Z 2024-01-30T20:29:25Z 2023-07-07 2024-01-28T08:00:28Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/153427 Hisham Alghamdi, Chika Maduabuchi, Kingsley Okoli, et al., “Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks,” International Journal of Energy Research, vol. 2023, Article ID 6927245, 18 pages, 2023. doi:10.1155/2023/6927245 en http://dx.doi.org/10.1155/2023/6927245 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ Copyright © 2023 Hisham Alghamdi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. application/pdf Hindawi |
| spellingShingle | Alghamdi, Hisham Maduabuchi, Chika Okoli, Kingsley Albaker, Abdullah Alatawi, Ibrahim Alsafran, Ahmed S. Alkhedher, Mohammad Chen, Wei-Hsin Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title | Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title_full | Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title_fullStr | Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title_full_unstemmed | Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title_short | Smart Optimization of Semiconductors in Photovoltaic-Thermoelectric Systems Using Recurrent Neural Networks |
| title_sort | smart optimization of semiconductors in photovoltaic thermoelectric systems using recurrent neural networks |
| url | https://hdl.handle.net/1721.1/153427 |
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