Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models
Solar irradiation prediction including Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) is a useful technique for assessing the solar energy potential at specific locations. This study used five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) to pre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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D. G. Pylarinos
2023-08-01
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| Series: | Engineering, Technology & Applied Science Research |
| Subjects: | |
| Online Access: | https://etasr.com/index.php/ETASR/article/view/6131 |
| _version_ | 1797745995553964032 |
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| author | Youssef Kassem Huseyin Camur Mustapha Tanimu Adamu Takudzwa Chikowero Terry Apreala |
| author_facet | Youssef Kassem Huseyin Camur Mustapha Tanimu Adamu Takudzwa Chikowero Terry Apreala |
| author_sort | Youssef Kassem |
| collection | DOAJ |
| description | Solar irradiation prediction including Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) is a useful technique for assessing the solar energy potential at specific locations. This study used five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) to predict GHI and DNI in Africa. Additionally, a hybrid model combining MLR and ANNs was proposed to predict both GHI and DNI and improve the accuracy of individual ANN models. Solar radiation (GHI and DNI) and global meteorological data from 85 cities with different climatic conditions over Africa during 2001-2020 were used to train and test the models developed. The Pearson correlation coefficient was used to identify the most influential input variables to predict GHI and DNI. Two scenarios were proposed to achieve the goal, each with different input variables. The first scenario used influential input parameters, while the second incorporated geographical coordinates to assess their impact on solar radiation prediction accuracy. The results revealed that the suggested linear-nonlinear hybrid models outperformed all other models in terms of prediction accuracy. Moreover, the investigation revealed that geographical coordinates have a minimal impact on the prediction of solar radiation.
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| first_indexed | 2024-03-12T15:30:45Z |
| format | Article |
| id | doaj.art-aa47a205831f4f079bedee5dda3d1391 |
| institution | Directory Open Access Journal |
| issn | 2241-4487 1792-8036 |
| language | English |
| last_indexed | 2024-03-12T15:30:45Z |
| publishDate | 2023-08-01 |
| publisher | D. G. Pylarinos |
| record_format | Article |
| series | Engineering, Technology & Applied Science Research |
| spelling | doaj.art-aa47a205831f4f079bedee5dda3d13912023-08-10T05:33:17ZengD. G. PylarinosEngineering, Technology & Applied Science Research2241-44871792-80362023-08-0113410.48084/etasr.6131Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid ModelsYoussef Kassem0Huseyin Camur1Mustapha Tanimu Adamu2Takudzwa Chikowero3Terry Apreala4Kassem Youssef Department of Mechanical Engineering, Engineering Faculty, Near East University, Cyprus | Energy, Environment, and Water Research Center, Near East University, Cyprus Department of Mechanical Engineering, Engineering Faculty, Near East University, CyprusDepartment of Mechanical Engineering, Engineering Faculty, Near East University, CyprusDepartment of Mechanical Engineering, Engineering Faculty, Near East University, CyprusDepartment of Mechanical Engineering, Engineering Faculty, Near East University, CyprusSolar irradiation prediction including Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) is a useful technique for assessing the solar energy potential at specific locations. This study used five Artificial Neural Network (ANN) models and Multiple Linear Regression (MLR) to predict GHI and DNI in Africa. Additionally, a hybrid model combining MLR and ANNs was proposed to predict both GHI and DNI and improve the accuracy of individual ANN models. Solar radiation (GHI and DNI) and global meteorological data from 85 cities with different climatic conditions over Africa during 2001-2020 were used to train and test the models developed. The Pearson correlation coefficient was used to identify the most influential input variables to predict GHI and DNI. Two scenarios were proposed to achieve the goal, each with different input variables. The first scenario used influential input parameters, while the second incorporated geographical coordinates to assess their impact on solar radiation prediction accuracy. The results revealed that the suggested linear-nonlinear hybrid models outperformed all other models in terms of prediction accuracy. Moreover, the investigation revealed that geographical coordinates have a minimal impact on the prediction of solar radiation. https://etasr.com/index.php/ETASR/article/view/6131direct normal irradiationhybrid modelglobal horizontal irradiationmultiple linear regressionartificial neural networks |
| spellingShingle | Youssef Kassem Huseyin Camur Mustapha Tanimu Adamu Takudzwa Chikowero Terry Apreala Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models Engineering, Technology & Applied Science Research direct normal irradiation hybrid model global horizontal irradiation multiple linear regression artificial neural networks |
| title | Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models |
| title_full | Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models |
| title_fullStr | Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models |
| title_full_unstemmed | Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models |
| title_short | Prediction of Solar Irradiation in Africa using Linear-Nonlinear Hybrid Models |
| title_sort | prediction of solar irradiation in africa using linear nonlinear hybrid models |
| topic | direct normal irradiation hybrid model global horizontal irradiation multiple linear regression artificial neural networks |
| url | https://etasr.com/index.php/ETASR/article/view/6131 |
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