Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods
The present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/345 |
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| author | Salim Kılıç Ertuğrul Adıgüzel Erkan Atmaca |
| author_facet | Salim Kılıç Ertuğrul Adıgüzel Erkan Atmaca |
| author_sort | Salim Kılıç |
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| description | The present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which were divided into clean and dirty groups. The following variables were monitored and recorded for a period of six months: radiation, panel temperature, air temperature, wind speed, humidity, pressure, and ultraviolet (UV) radiation. Additionally, current, voltage, and power were recorded. These measurements were taken during the production of energy by PV panels. Monocrystalline PV panels exhibited an 8.6% increase in energy efficiency, while polycrystalline PV panels demonstrated a 6.2% increase, following the collection and cleaning of data in accordance with the IEC 61724 standard. Six distinct machine learning regression analyses were conducted on the dataset. The results were compared using the Root Mean Square Error (RMSE) and the coefficient of determination (R<sup>2</sup>). The Random Forest model demonstrated the greatest predictive success, while the Support Vector Regression (SVR) model exhibited the lowest performance. |
| format | Article |
| id | doaj-art-059827d1ffff428da7be004d449559e2 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-059827d1ffff428da7be004d449559e22025-01-10T13:15:14ZengMDPI AGApplied Sciences2076-34172025-01-0115134510.3390/app15010345Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression MethodsSalim Kılıç0Ertuğrul Adıgüzel1Erkan Atmaca2Department of Electrical and Electronics Engineering, Faculty of Engineering, İstanbul University—Cerrahpaşa, 34320 İstanbul, TürkiyeDepartment of Electrical and Electronics Engineering, Faculty of Engineering, İstanbul University—Cerrahpaşa, 34320 İstanbul, TürkiyeDepartment of Electrical and Electronics Engineering, Faculty of Engineering, İstanbul University—Cerrahpaşa, 34320 İstanbul, TürkiyeThe present study employs machine learning regression analyses to investigate the efficiency of photovoltaic (PV) panels utilizing solar energy under the influence of environmental factors. The experimental study was conducted on two 100-watt monocrystalline and two polycrystalline PV panels, which were divided into clean and dirty groups. The following variables were monitored and recorded for a period of six months: radiation, panel temperature, air temperature, wind speed, humidity, pressure, and ultraviolet (UV) radiation. Additionally, current, voltage, and power were recorded. These measurements were taken during the production of energy by PV panels. Monocrystalline PV panels exhibited an 8.6% increase in energy efficiency, while polycrystalline PV panels demonstrated a 6.2% increase, following the collection and cleaning of data in accordance with the IEC 61724 standard. Six distinct machine learning regression analyses were conducted on the dataset. The results were compared using the Root Mean Square Error (RMSE) and the coefficient of determination (R<sup>2</sup>). The Random Forest model demonstrated the greatest predictive success, while the Support Vector Regression (SVR) model exhibited the lowest performance.https://www.mdpi.com/2076-3417/15/1/345photovoltaic (PV)machine learningregression analysesefficiencyenvironmental factors |
| spellingShingle | Salim Kılıç Ertuğrul Adıgüzel Erkan Atmaca Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods Applied Sciences photovoltaic (PV) machine learning regression analyses efficiency environmental factors |
| title | Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods |
| title_full | Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods |
| title_fullStr | Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods |
| title_full_unstemmed | Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods |
| title_short | Analysis of the Performance of Different Types of PV Panels in Spring and Summer Using Regression Methods |
| title_sort | analysis of the performance of different types of pv panels in spring and summer using regression methods |
| topic | photovoltaic (PV) machine learning regression analyses efficiency environmental factors |
| url | https://www.mdpi.com/2076-3417/15/1/345 |
| work_keys_str_mv | AT salimkılıc analysisoftheperformanceofdifferenttypesofpvpanelsinspringandsummerusingregressionmethods AT ertugruladıguzel analysisoftheperformanceofdifferenttypesofpvpanelsinspringandsummerusingregressionmethods AT erkanatmaca analysisoftheperformanceofdifferenttypesofpvpanelsinspringandsummerusingregressionmethods |