Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study
The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Reliza...
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| Format: | Article |
| Language: | English |
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University of El Oued
2022-12-01
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| Series: | International Journal of Energetica |
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| Online Access: | https://www.ijeca.info/index.php/IJECA/article/view/198 |
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| author | Dahmani Abdennasser Yamina Ammi Salah Hanini |
| author_facet | Dahmani Abdennasser Yamina Ammi Salah Hanini |
| author_sort | Dahmani Abdennasser |
| collection | DOAJ |
| description | The global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 % of whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr) training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions. |
| format | Article |
| id | doaj-art-7647002a6c394ec8981b9099e537e821 |
| institution | Kabale University |
| issn | 2543-3717 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | University of El Oued |
| record_format | Article |
| series | International Journal of Energetica |
| spelling | doaj-art-7647002a6c394ec8981b9099e537e8212025-08-20T03:35:16ZengUniversity of El OuedInternational Journal of Energetica2543-37172022-12-0172818120Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis studyDahmani Abdennasser0Yamina Ammi1Salah Hanini2Department of Mechanical Engineering, University Ahmad Zabana of RelizaneLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of MedeaLaboratory of Biomaterials and Transport Phenomena (LBMPT), University of MedeaThe global solar radiation prediction is the most necessary part of the project and performance of solar energy applications. The objective of the present work is to predict global solar radiation (GSR) received on the horizontal surface using an artificial neural network (ANN). For the city (Relizane) in the west region of Algeria. The inputs used in the neural network are: time (h), day, month, year, temperature (k), relative humidity (%), pressure (mbar), wind speed (m/s), wind direction (°), and rainfall (kg/m2). The neural network-optimal model was trained and tested using 80 %, and 20 % of whole data, respectively. The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr) training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. The results demonstrate proper ANN’s predictions with a root mean square error (RMSE) of less than 0.50 (Wh/m2) and coefficient of correlation (R) higher than 0.98, which can be considered very acceptable. This model can be used for designing solar energy systems in the hottest regions.https://www.ijeca.info/index.php/IJECA/article/view/198prediction, global solar radiation, artificial neural networks, relizane |
| spellingShingle | Dahmani Abdennasser Yamina Ammi Salah Hanini Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study International Journal of Energetica prediction, global solar radiation, artificial neural networks, relizane |
| title | Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study |
| title_full | Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study |
| title_fullStr | Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study |
| title_full_unstemmed | Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study |
| title_short | Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study |
| title_sort | neural network for prediction solar radiation in relizane region algeria analysis study |
| topic | prediction, global solar radiation, artificial neural networks, relizane |
| url | https://www.ijeca.info/index.php/IJECA/article/view/198 |
| work_keys_str_mv | AT dahmaniabdennasser neuralnetworkforpredictionsolarradiationinrelizaneregionalgeriaanalysisstudy AT yaminaammi neuralnetworkforpredictionsolarradiationinrelizaneregionalgeriaanalysisstudy AT salahhanini neuralnetworkforpredictionsolarradiationinrelizaneregionalgeriaanalysisstudy |