Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System
Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Hindawi
2023
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| Online Access: | https://hdl.handle.net/1721.1/151044 |
| _version_ | 1826214648983060480 |
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| author | Alghamdi, Hisham Maduabuchi, Chika Yusuf, Aminu Al-Dahidi, Sameer Albaker, Abdullah Alatawi, Ibrahim Alsenani, Theyab R. Alsafran, Ahmed S. AlAqil, Mohammed Alkhedher, Mohammad |
| 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 Yusuf, Aminu Al-Dahidi, Sameer Albaker, Abdullah Alatawi, Ibrahim Alsenani, Theyab R. Alsafran, Ahmed S. AlAqil, Mohammed Alkhedher, Mohammad |
| author_sort | Alghamdi, Hisham |
| collection | MIT |
| description | Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities (). Although it is difficult to attain these ideal conditions without complex material engineering, the low implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model. |
| first_indexed | 2024-09-23T16:08:50Z |
| format | Article |
| id | mit-1721.1/151044 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:08:50Z |
| publishDate | 2023 |
| publisher | Hindawi |
| record_format | dspace |
| spelling | mit-1721.1/1510442024-01-22T18:23:25Z Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System Alghamdi, Hisham Maduabuchi, Chika Yusuf, Aminu Al-Dahidi, Sameer Albaker, Abdullah Alatawi, Ibrahim Alsenani, Theyab R. Alsafran, Ahmed S. AlAqil, Mohammed Alkhedher, Mohammad Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Previous theoretical research efforts which were validated by experimental findings demonstrated the thermo-economic benefits of the hybrid concentrated photovoltaic-thermoelectric (CPV-TE) system over the stand-alone CPV. However, the operating conditions and TE material properties for maximum CPV-TE performance may differ from those required in a standalone thermoelectric module (TEM). For instance, a high-performing TEM requires TE materials with high Seebeck coefficients and electrical conductivities, and at the same time, low thermal conductivities (). Although it is difficult to attain these ideal conditions without complex material engineering, the low implies a high thermal resistance and temperature difference across the TEM which raises the PV backplate’s temperature in a hybrid CPV-TE operation. The increased PV temperature may reduce the overall system’s thermodynamic performance. To understand this phenomenon, a study is needed to guide researchers in choosing the best TE material for an optimal operation of a CPV-TE system. However, no prior research effort has been made to this effect. One method of finding the optimum TE material property is to parametrically vary one or more transport parameters until an optimum point is determined. However, this method is time-consuming and inefficient since a global optimum may not be found, especially when large incremental step sizes are used. This study provides a better way to solve this problem by using a multiobjective optimization genetic algorithm (MOGA) which is fast and reliable and ensures that the global optimum is obtained. After the optimization has been conducted, the best performing conditions for maximum CPV-TE energy, exergy, and environmental (3E) performance are selected using the technique for order performance by similarity to ideal solution (TOPSIS) decision algorithm. Finally, the optimization workflow is deployed for 7000 test cases generated from 10 features using the optimal machine learning (ML) algorithm. The result of the optimization chosen by the TOPSIS decision-making method generated an output power, exergy efficiency, and CO2 saving of 44.6 W, 18.3%, and 0.17 g/day, respectively. Furthermore, among other ML algorithms, the Gaussian process regression was the most accurate in learning the CPV-TE performance dataset, although it required more computational effort than some algorithms like the linear regression model. 2023-07-10T11:14:37Z 2023-07-10T11:14:37Z 2023-03-15 2023-07-09T08:00:12Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/151044 Hisham Alghamdi, Chika Maduabuchi, Aminu Yusuf, et al., “Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System,” International Journal of Energy Research, vol. 2023, Article ID 6418897, 22 pages, 2023. doi:10.1155/2023/6418897 en http://dx.doi.org/10.1155/2023/6418897 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. text/xml application/pdf Hindawi Hindawi |
| spellingShingle | Alghamdi, Hisham Maduabuchi, Chika Yusuf, Aminu Al-Dahidi, Sameer Albaker, Abdullah Alatawi, Ibrahim Alsenani, Theyab R. Alsafran, Ahmed S. AlAqil, Mohammed Alkhedher, Mohammad Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title | Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title_full | Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title_fullStr | Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title_full_unstemmed | Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title_short | Multiobjective Optimization and Machine Learning Algorithms for Forecasting the 3E Performance of a Concentrated Photovoltaic-Thermoelectric System |
| title_sort | multiobjective optimization and machine learning algorithms for forecasting the 3e performance of a concentrated photovoltaic thermoelectric system |
| url | https://hdl.handle.net/1721.1/151044 |
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