Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types
Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell,...
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Hindawi
2023
|
| Online Access: | https://hdl.handle.net/1721.1/148616 |
| _version_ | 1811092495869673472 |
|---|---|
| author | Alghamdi, Hisham Maduabuchi, Chika Albaker, Abdullah Almalaq, Abdulaziz Alsuwian, Turki Alatawi, Ibrahim |
| 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 Albaker, Abdullah Almalaq, Abdulaziz Alsuwian, Turki Alatawi, Ibrahim |
| author_sort | Alghamdi, Hisham |
| collection | MIT |
| description | Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of and high training and testing regression of 100% and 99.98%, respectively. |
| first_indexed | 2024-09-23T15:19:05Z |
| format | Article |
| id | mit-1721.1/148616 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:19:05Z |
| publishDate | 2023 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | mit-1721.1/1486162024-01-12T18:39:35Z Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types Alghamdi, Hisham Maduabuchi, Chika Albaker, Abdullah Almalaq, Abdulaziz Alsuwian, Turki Alatawi, Ibrahim Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Since the solar photovoltaic-thermoelectric (PV-TE) is an upcoming technology, the current literature on PV-TEs have failed to thoroughly investigate the effects of different solar cell types on the PV-TE’s performance. Such parametric study becomes necessary since the properties of the solar cell, characterized by the cell temperature coefficient and reference efficiency, determine the overall performance of the PV-TE. Further, the design information obtained from numerical solvers is minimal due to the high time and computational energy required to optimize the photovoltaic-thermoelectric (PV-TE) performance. This problem severely affects the ease with which useful design information can be drawn from current methods to design high-performance PVs. Additionally, to reduce the complexity of the existing numerical model, the previous models have introduced some principal assumptions that have significantly affected the accuracy of the numerical results. Finally, the numerical model has neglected several real-life operating conditions since they increase the model complexity. For the first time, deep neural networks are proposed to predict the photovoltaic-thermoelectric performance designed with 3 different crystalline solar cells as a perfect replacement for the inefficient numerical methods used to analyze the hybrid system. After that, the data generated by the numerical solver is fed to an optimum 3-layer deep neural network with 20 neurons per layer to forecast the hybrid system’s performance efficiently. The training of the neural network is governed by Bayesian regularization and the performance of the model is evaluated using the mean squared error, coefficient of determination, and training time. Results show that the deep neural network accurately learned the results that took the conventional numerical solver 1,600 mins to generate in just 12 min and 27 s, making the proposed network 128.51 times faster than the traditional numerical method. Furthermore, the accuracy of the network is demonstrated by the low mean squared error of and high training and testing regression of 100% and 99.98%, respectively. 2023-03-20T12:15:25Z 2023-03-20T12:15:25Z 2023-02-10 2023-03-19T08:00:17Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/148616 Hisham Alghamdi, Chika Maduabuchi, Abdullah Albaker, Abdulaziz Almalaq, Turki Alsuwian, and Ibrahim Alatawi, “Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types,” International Journal of Energy Research, vol. 2023, Article ID 1990593, 26 pages, 2023. doi:10.1155/2023/1990593 en http://dx.doi.org/10.1155/2023/1990593 Attribution 4.0 International 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 Hindawi |
| spellingShingle | Alghamdi, Hisham Maduabuchi, Chika Albaker, Abdullah Almalaq, Abdulaziz Alsuwian, Turki Alatawi, Ibrahim Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title | Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title_full | Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title_fullStr | Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title_full_unstemmed | Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title_short | Machine Learning Performance Prediction of a Solar Photovoltaic-Thermoelectric System with Various Crystalline Silicon Cell Types |
| title_sort | machine learning performance prediction of a solar photovoltaic thermoelectric system with various crystalline silicon cell types |
| url | https://hdl.handle.net/1721.1/148616 |
| work_keys_str_mv | AT alghamdihisham machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes AT maduabuchichika machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes AT albakerabdullah machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes AT almalaqabdulaziz machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes AT alsuwianturki machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes AT alatawiibrahim machinelearningperformancepredictionofasolarphotovoltaicthermoelectricsystemwithvariouscrystallinesiliconcelltypes |