Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications
The choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they p...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/4493116 |
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| author | Bibhu Prasad Ganthia Sudheer Hanumanthakari Hemachandra Gudimindla Harishchander Anandaram M. Siva Ramkumar Monalisa Mohanty S. Raja Gopal Atul Sarojwal Kibrom Menasbo Hadish |
| author_facet | Bibhu Prasad Ganthia Sudheer Hanumanthakari Hemachandra Gudimindla Harishchander Anandaram M. Siva Ramkumar Monalisa Mohanty S. Raja Gopal Atul Sarojwal Kibrom Menasbo Hadish |
| author_sort | Bibhu Prasad Ganthia |
| collection | DOAJ |
| description | The choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. Unfortunately, this feature is very rare on various solar power panels; however, ignoring it can reduce the performance of the panels to unacceptable levels. The fact is that the angle of incidence of rays on the surface greatly affects the reflection coefficient and, consequently, the role of unacceptable solar energy. In this paper, a smart energy harvesting model was proposed. In the case of glass, when the angle of incidence varies vertically from its surface to 30, the reflection coefficient is practically unchanged and slightly less than 5%, i.e., more than 95% of the radiation goes inwards. Furthermore, the reflection increase is noticeable, and the area of the reflected radiation by 60 doubles to almost 10%. At an angle of incidence of 70, it reflects 20% of the radiation, and at 80, 40%. For most other objects, the dependence of the reflection magnitude on the angle of incidence is approximately the same. |
| format | Article |
| id | doaj-art-4fcc8640c36647dcbaf8b2933db0bf59 |
| institution | OA Journals |
| issn | 1687-529X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-4fcc8640c36647dcbaf8b2933db0bf592025-08-20T02:05:38ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/4493116Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel ApplicationsBibhu Prasad Ganthia0Sudheer Hanumanthakari1Hemachandra Gudimindla2Harishchander Anandaram3M. Siva Ramkumar4Monalisa Mohanty5S. Raja Gopal6Atul Sarojwal7Kibrom Menasbo Hadish8Department of Electrical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electrical and Electronics EngineeringCentre for Computational Engineering and NetworkingDepartment of Electrical and Electronics EngineeringDepartment of Electrical and Electronics EngineeringDepartment of Electronics & Communications EngineeringDepartment of Electrical EngineeringFaculty of Mechanical EngineeringThe choice of the optimal orientation of the solar panels is by far one of the most important issues in the practical application of solar installations. The use of phase changing materials (PCMs) is an efficient approach of storing solar thermal energy. Because PCMs are isothermal in nature, they provide better density energy storage and the capacity to function across a wide temperature range. Unfortunately, this feature is very rare on various solar power panels; however, ignoring it can reduce the performance of the panels to unacceptable levels. The fact is that the angle of incidence of rays on the surface greatly affects the reflection coefficient and, consequently, the role of unacceptable solar energy. In this paper, a smart energy harvesting model was proposed. In the case of glass, when the angle of incidence varies vertically from its surface to 30, the reflection coefficient is practically unchanged and slightly less than 5%, i.e., more than 95% of the radiation goes inwards. Furthermore, the reflection increase is noticeable, and the area of the reflected radiation by 60 doubles to almost 10%. At an angle of incidence of 70, it reflects 20% of the radiation, and at 80, 40%. For most other objects, the dependence of the reflection magnitude on the angle of incidence is approximately the same.http://dx.doi.org/10.1155/2022/4493116 |
| spellingShingle | Bibhu Prasad Ganthia Sudheer Hanumanthakari Hemachandra Gudimindla Harishchander Anandaram M. Siva Ramkumar Monalisa Mohanty S. Raja Gopal Atul Sarojwal Kibrom Menasbo Hadish Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications International Journal of Photoenergy |
| title | Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications |
| title_full | Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications |
| title_fullStr | Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications |
| title_full_unstemmed | Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications |
| title_short | Machine Learning Strategy to Achieve Maximum Energy Harvesting and Monitoring Method for Solar Photovoltaic Panel Applications |
| title_sort | machine learning strategy to achieve maximum energy harvesting and monitoring method for solar photovoltaic panel applications |
| url | http://dx.doi.org/10.1155/2022/4493116 |
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