Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability
ABSTRACT In recent years, photovoltaic (PV) solar energy has played a crucial role in the global transition toward renewable energy, contributing to 46% of the electric capacity. It has emerged as a primary source; however, optimizing energy utilization and solar panel efficiency to maximize absorbe...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Energy Science & Engineering |
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| Online Access: | https://doi.org/10.1002/ese3.1979 |
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| author | Li Bin Muhammad Shahzad Muhammad Farhan Muhammad Sanaullah Khan Mubaarak Abdulrahman Abdu Saif Girmaw Teshager Bitew |
| author_facet | Li Bin Muhammad Shahzad Muhammad Farhan Muhammad Sanaullah Khan Mubaarak Abdulrahman Abdu Saif Girmaw Teshager Bitew |
| author_sort | Li Bin |
| collection | DOAJ |
| description | ABSTRACT In recent years, photovoltaic (PV) solar energy has played a crucial role in the global transition toward renewable energy, contributing to 46% of the electric capacity. It has emerged as a primary source; however, optimizing energy utilization and solar panel efficiency to maximize absorbed solar radiation remains a significant challenge. Additionally, it addresses the optimization of solar energy generation and the mitigation of potential overheating issues in dual‐axis solar tracking systems. Despite its importance, PV power generation is hindered by uncertainty and intermittency, posing obstacles to achieving a stable and reliable power supply. This research introduces an innovative synthesis method for a typical solar radiation year (TSRY) based on K‐means clustering to maximize energy harvest. The K‐means algorithm, a fundamental image processing technique, is utilized to classify images into distinct groups. This approach enhances energy generation potential, panel efficiency, and the long‐term sustainability of solar energy systems compared to conventional methods. |
| format | Article |
| id | doaj-art-e6dd2c1d9f35479fb445e2b333b8c08e |
| institution | Kabale University |
| issn | 2050-0505 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Energy Science & Engineering |
| spelling | doaj-art-e6dd2c1d9f35479fb445e2b333b8c08e2025-01-21T11:38:24ZengWileyEnergy Science & Engineering2050-05052025-01-0113119120210.1002/ese3.1979Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and ViabilityLi Bin0Muhammad Shahzad1Muhammad Farhan2Muhammad Sanaullah Khan3Mubaarak Abdulrahman Abdu Saif4Girmaw Teshager Bitew5School of Electrical and Electronic Engineering North China Electric Power University Beijing People's Republic of ChinaDepartment of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan PakistanDepartment of Electrical Engineering and Technology Government College University Faisalabad Faisalabad PakistanDepartment of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan PakistanRenewable Energy Department, Faculty of Engineering and Information Technology Taiz University Taiz YemenFaculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Amhara EthiopiaABSTRACT In recent years, photovoltaic (PV) solar energy has played a crucial role in the global transition toward renewable energy, contributing to 46% of the electric capacity. It has emerged as a primary source; however, optimizing energy utilization and solar panel efficiency to maximize absorbed solar radiation remains a significant challenge. Additionally, it addresses the optimization of solar energy generation and the mitigation of potential overheating issues in dual‐axis solar tracking systems. Despite its importance, PV power generation is hindered by uncertainty and intermittency, posing obstacles to achieving a stable and reliable power supply. This research introduces an innovative synthesis method for a typical solar radiation year (TSRY) based on K‐means clustering to maximize energy harvest. The K‐means algorithm, a fundamental image processing technique, is utilized to classify images into distinct groups. This approach enhances energy generation potential, panel efficiency, and the long‐term sustainability of solar energy systems compared to conventional methods.https://doi.org/10.1002/ese3.1979distributed generationK‐means clusteringmaximum power point trackingphotovoltic energyrenewable energysustainable energy |
| spellingShingle | Li Bin Muhammad Shahzad Muhammad Farhan Muhammad Sanaullah Khan Mubaarak Abdulrahman Abdu Saif Girmaw Teshager Bitew Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability Energy Science & Engineering distributed generation K‐means clustering maximum power point tracking photovoltic energy renewable energy sustainable energy |
| title | Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability |
| title_full | Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability |
| title_fullStr | Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability |
| title_full_unstemmed | Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability |
| title_short | Optimizing Solar Energy Harvesting: A K‐Means Clustering Approach for Enhanced Efficiency and Viability |
| title_sort | optimizing solar energy harvesting a k means clustering approach for enhanced efficiency and viability |
| topic | distributed generation K‐means clustering maximum power point tracking photovoltic energy renewable energy sustainable energy |
| url | https://doi.org/10.1002/ese3.1979 |
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