A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System
Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination o...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2022/2845755 |
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| author | Kalapala Prasad J. Samson Isaac P. Ponsudha N. Nithya Santaji Krishna Shinde S. Raja Gopal Atul Sarojwal K. Karthikumar Kibrom Menasbo Hadish |
| author_facet | Kalapala Prasad J. Samson Isaac P. Ponsudha N. Nithya Santaji Krishna Shinde S. Raja Gopal Atul Sarojwal K. Karthikumar Kibrom Menasbo Hadish |
| author_sort | Kalapala Prasad |
| collection | DOAJ |
| description | Performance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results. |
| format | Article |
| id | doaj-art-dd0f38d4bd7e4d66bb146b53837a5556 |
| 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-dd0f38d4bd7e4d66bb146b53837a55562025-08-20T02:21:37ZengWileyInternational Journal of Photoenergy1687-529X2022-01-01202210.1155/2022/2845755A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power SystemKalapala Prasad0J. Samson Isaac1P. Ponsudha2N. Nithya3Santaji Krishna Shinde4S. Raja Gopal5Atul Sarojwal6K. Karthikumar7Kibrom Menasbo Hadish8Department of Mechanical EngineeringDepartment of Biomedical EngineeringDepartment of Electronics and Communication EngineeringDepartment of Electronics and Communication EngineeringComputer Engineering DepartmentDepartment of Electronics & Communications EngineeringDepartment of Electrical EngineeringDepartment of Electrical and Electronics EngineeringFaculty of Mechanical EngineeringPerformance, cost, and aesthetics are all difficult to beat in today’s expanding distributed rooftop solar sector, and flat-plate PV is no exception. Photovoltaics will be able to take advantage of some of their most significant advantages as a result of this marketplace, including the elimination of transmission losses and the generation of power at the point of sale. Concentrated photovoltaic (CPV) technology, on the other hand, represents a viable alternative in the quest for ever-lower normalised energy costs and ever-shorter energy payback times. Material, components, and manufacturing techniques from allied sectors, particularly the power electronics industry, have been adapted to lower system costs and time-to-market for the system under development. The LFR is less than 30 mm wide to maximise thermal efficiency, and a densely packed cell array has been used to maximise electrical output. The Matlab simulations show that the proposed machine learning-based LFR technique has a greater concentration rate than the present LFR method, as demonstrated by the results.http://dx.doi.org/10.1155/2022/2845755 |
| spellingShingle | Kalapala Prasad J. Samson Isaac P. Ponsudha N. Nithya Santaji Krishna Shinde S. Raja Gopal Atul Sarojwal K. Karthikumar Kibrom Menasbo Hadish A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System International Journal of Photoenergy |
| title | A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System |
| title_full | A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System |
| title_fullStr | A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System |
| title_full_unstemmed | A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System |
| title_short | A Machine Learning-Based Novel Energy Optimization Algorithm in a Photovoltaic Solar Power System |
| title_sort | machine learning based novel energy optimization algorithm in a photovoltaic solar power system |
| url | http://dx.doi.org/10.1155/2022/2845755 |
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