Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks
This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim...
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| Format: | Article |
| Language: | English |
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OICC Press
2025-06-01
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| Series: | Majlesi Journal of Electrical Engineering |
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| Online Access: | https://oiccpress.com/mjee/article/view/8309 |
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| author | Cempaka Amalin Mahadzir Ahmad Fateh Mohamad Nor Siti Amely Jumaat Noor Syahirah Ahmad Safawi |
| author_facet | Cempaka Amalin Mahadzir Ahmad Fateh Mohamad Nor Siti Amely Jumaat Noor Syahirah Ahmad Safawi |
| author_sort | Cempaka Amalin Mahadzir |
| collection | DOAJ |
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This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of this research is to develop ANN algorithms that anticipate solar power output and enhance the structure of them by incorporating the derating factor due to dirt (kdirt) into account. The effectiveness and dependability of the ANN are determined using MATLAB software. By comparing the Mean Squared Error (MSE) of four different values of derating factor due to dirt which are 0.8, 0.88, 0.9 and 0.98 in ANN predictions comprehend with 4 input layers and 10 hidden layers. Direct data input is obtained through a photovoltaic solar panel at Universiti Tun Hussein Onn Malaysia (UTHM). Comparative analysis also has been carried out after the results has been obtained from the mathematical equations. The daily solar power output predictions are effectively achieved by the deployed ANN. As the result, the optimal kdirt has been selected which is 0.8 based on its ability to produce the most accurate ANN predictions than the other values of kdirt.
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| format | Article |
| id | doaj-art-4a5b8d8e98664e0c9b190c9059eeefbf |
| institution | Kabale University |
| issn | 2345-377X 2345-3796 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | OICC Press |
| record_format | Article |
| series | Majlesi Journal of Electrical Engineering |
| spelling | doaj-art-4a5b8d8e98664e0c9b190c9059eeefbf2025-08-20T03:33:27ZengOICC PressMajlesi Journal of Electrical Engineering2345-377X2345-37962025-06-01192 (June 2025)10.57647/j.mjee.2025.1902.36Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural NetworksCempaka Amalin MahadzirAhmad Fateh Mohamad NorSiti Amely JumaatNoor Syahirah Ahmad Safawi This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of this research is to develop ANN algorithms that anticipate solar power output and enhance the structure of them by incorporating the derating factor due to dirt (kdirt) into account. The effectiveness and dependability of the ANN are determined using MATLAB software. By comparing the Mean Squared Error (MSE) of four different values of derating factor due to dirt which are 0.8, 0.88, 0.9 and 0.98 in ANN predictions comprehend with 4 input layers and 10 hidden layers. Direct data input is obtained through a photovoltaic solar panel at Universiti Tun Hussein Onn Malaysia (UTHM). Comparative analysis also has been carried out after the results has been obtained from the mathematical equations. The daily solar power output predictions are effectively achieved by the deployed ANN. As the result, the optimal kdirt has been selected which is 0.8 based on its ability to produce the most accurate ANN predictions than the other values of kdirt. https://oiccpress.com/mjee/article/view/8309Power predictionSolar outputANNMSEDerating factor |
| spellingShingle | Cempaka Amalin Mahadzir Ahmad Fateh Mohamad Nor Siti Amely Jumaat Noor Syahirah Ahmad Safawi Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks Majlesi Journal of Electrical Engineering Power prediction Solar output ANN MSE Derating factor |
| title | Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks |
| title_full | Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks |
| title_fullStr | Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks |
| title_full_unstemmed | Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks |
| title_short | Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks |
| title_sort | optimizing photovoltaic power prediction using computational methods and artificial neural networks |
| topic | Power prediction Solar output ANN MSE Derating factor |
| url | https://oiccpress.com/mjee/article/view/8309 |
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