Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module
In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including:...
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middle technical university
2024-03-01
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| Series: | Journal of Techniques |
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| Online Access: | https://journal.mtu.edu.iq/index.php/MTU/article/view/895 |
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| author | Hussain Hamdi Khalaf Ali Nasser Hussain Zuhair S. Al-Sagar Abdulrahman Th. Mohammad Hilal A. Fadhil |
| author_facet | Hussain Hamdi Khalaf Ali Nasser Hussain Zuhair S. Al-Sagar Abdulrahman Th. Mohammad Hilal A. Fadhil |
| author_sort | Hussain Hamdi Khalaf |
| collection | DOAJ |
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In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including: (solar radiation, ambient temperature, and wind speed), the output electrical characteristics of the PV module (voltage, current, power), and module temperature (were measured. Therefore, the dataset of the ANN system consists of four input and one output parameter. Furthermore, the structure of ANN includes a single hidden layer with a backward propagation technique. The main goal of this study was to optimize the number of neurons in the training process. The evaluation of the ANN model depended on the determination coefficient (R) and Root Mean Squared Error (RMSE). The obtained results show that the architecture of ANN is appropriate for predicting the power generated from the PV module. The developed ANN model has good accuracy. Where the MSE is 0.002747 at epoch 9 in the model. Furthermore, the R is recorded as 0.99078, 0.98254, 0.99125, and 0.99005 for training, testing, validation, and all respectively in the proposed model. In addition, the optimization number of neurons in the hidden layer gave sufficient accuracy without referring to the choice of the number of neurons by using the trial-and-error method that most researchers relied.
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| format | Article |
| id | doaj-art-183529098be64591abcf75c26e55dd5e |
| institution | Kabale University |
| issn | 1818-653X 2708-8383 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | middle technical university |
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| series | Journal of Techniques |
| spelling | doaj-art-183529098be64591abcf75c26e55dd5e2025-01-19T10:58:56Zengmiddle technical universityJournal of Techniques1818-653X2708-83832024-03-016110.51173/jt.v6i1.895Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV ModuleHussain Hamdi Khalaf0Ali Nasser Hussain1Zuhair S. Al-Sagar2Abdulrahman Th. Mohammad3Hilal A. Fadhil4Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Technical Institute / Baquba, Middle Technical University, Baghdad, Iraq.Technical Institute / Baquba, Middle Technical University, Baghdad, Iraq.Electrical and Computer Engineering Faculty, Sohar University, Sohar, Sultanate of Oman In this work, an Artificial Neural Network (ANN) with a backward-propagation technique was used to predict the power generation of the Photovoltaic (PV) module in weather conditions of Baghdad city-Iraq. Experiment tests were investigated in the summer of 2022. Three weather parameters, including: (solar radiation, ambient temperature, and wind speed), the output electrical characteristics of the PV module (voltage, current, power), and module temperature (were measured. Therefore, the dataset of the ANN system consists of four input and one output parameter. Furthermore, the structure of ANN includes a single hidden layer with a backward propagation technique. The main goal of this study was to optimize the number of neurons in the training process. The evaluation of the ANN model depended on the determination coefficient (R) and Root Mean Squared Error (RMSE). The obtained results show that the architecture of ANN is appropriate for predicting the power generated from the PV module. The developed ANN model has good accuracy. Where the MSE is 0.002747 at epoch 9 in the model. Furthermore, the R is recorded as 0.99078, 0.98254, 0.99125, and 0.99005 for training, testing, validation, and all respectively in the proposed model. In addition, the optimization number of neurons in the hidden layer gave sufficient accuracy without referring to the choice of the number of neurons by using the trial-and-error method that most researchers relied. https://journal.mtu.edu.iq/index.php/MTU/article/view/895ANNPV ModulePower GenerationSingle Hidden LayerNeurons |
| spellingShingle | Hussain Hamdi Khalaf Ali Nasser Hussain Zuhair S. Al-Sagar Abdulrahman Th. Mohammad Hilal A. Fadhil Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module Journal of Techniques ANN PV Module Power Generation Single Hidden Layer Neurons |
| title | Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module |
| title_full | Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module |
| title_fullStr | Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module |
| title_full_unstemmed | Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module |
| title_short | Optimization of Neurons Number in Artificial Neural Network Model for Predicting the Power Production of PV Module |
| title_sort | optimization of neurons number in artificial neural network model for predicting the power production of pv module |
| topic | ANN PV Module Power Generation Single Hidden Layer Neurons |
| url | https://journal.mtu.edu.iq/index.php/MTU/article/view/895 |
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