Hybrid Models for Daily Global Solar Radiation Assessment
Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiat...
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| Format: | Article |
| Language: | English |
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Universidade Federal de Viçosa (UFV)
2023-06-01
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| Series: | The Journal of Engineering and Exact Sciences |
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| Online Access: | https://periodicos.ufv.br/jcec/article/view/15926 |
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| _version_ | 1832569711000289280 |
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| author | Abdelkerim Souahlia Abdelhalim Rabehi Abdelazize Rabehi |
| author_facet | Abdelkerim Souahlia Abdelhalim Rabehi Abdelazize Rabehi |
| author_sort | Abdelkerim Souahlia |
| collection | DOAJ |
| description |
Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiation components due to the continuously changing climatic conditions. Usually, several input data predictors are used for the forecasting process, which can cause redundancy and correlation between data features. This work assesses a set of feature selection techniques to check their ability to select the relevant predictors and reduce redundant and irrelevant information. An Artificial Neural Network is used to fit the measured solar radiation based on the selected features. The developed model is evaluated through various objective evaluation metrics using historical data of three years measured at the Ghardaiaregion inAlgeria. Results show the effectiveness of the proposed method, where values of 0,0189, 0.0286, 5.4387, and 98.28% have been found as MABE, RMSE, nRMSE and r, respectively.
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| format | Article |
| id | doaj-art-20f1e55d539f4762bf353efeba942d46 |
| institution | Kabale University |
| issn | 2527-1075 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Universidade Federal de Viçosa (UFV) |
| record_format | Article |
| series | The Journal of Engineering and Exact Sciences |
| spelling | doaj-art-20f1e55d539f4762bf353efeba942d462025-02-02T19:55:07ZengUniversidade Federal de Viçosa (UFV)The Journal of Engineering and Exact Sciences2527-10752023-06-019410.18540/jcecvl9iss4pp15926-01eHybrid Models for Daily Global Solar Radiation Assessment Abdelkerim Souahlia0Abdelhalim Rabehi1Abdelazize Rabehi2Telecommunications and smart systems Laboratory, University of ZianeAchour, Djelfa 17000, Algeria.Telecommunications and smart systems Laboratory, University of ZianeAchour, Djelfa 17000, Algeria.Telecommunications and smart systems Laboratory, University of ZianeAchour, Djelfa 17000, Algeria. Daily solar radiation forecasting has recently become critical in developing solar energy and its integration into grid systems. Despite the huge number of proposed forecasting techniques, an accurate estimation remains a significant challenge because of the non-stationary variation of solar radiation components due to the continuously changing climatic conditions. Usually, several input data predictors are used for the forecasting process, which can cause redundancy and correlation between data features. This work assesses a set of feature selection techniques to check their ability to select the relevant predictors and reduce redundant and irrelevant information. An Artificial Neural Network is used to fit the measured solar radiation based on the selected features. The developed model is evaluated through various objective evaluation metrics using historical data of three years measured at the Ghardaiaregion inAlgeria. Results show the effectiveness of the proposed method, where values of 0,0189, 0.0286, 5.4387, and 98.28% have been found as MABE, RMSE, nRMSE and r, respectively. https://periodicos.ufv.br/jcec/article/view/15926Solar radiation, renewable energy, features selection, Forecasting, Artificial Neural Networks. |
| spellingShingle | Abdelkerim Souahlia Abdelhalim Rabehi Abdelazize Rabehi Hybrid Models for Daily Global Solar Radiation Assessment The Journal of Engineering and Exact Sciences Solar radiation, renewable energy, features selection, Forecasting, Artificial Neural Networks. |
| title | Hybrid Models for Daily Global Solar Radiation Assessment |
| title_full | Hybrid Models for Daily Global Solar Radiation Assessment |
| title_fullStr | Hybrid Models for Daily Global Solar Radiation Assessment |
| title_full_unstemmed | Hybrid Models for Daily Global Solar Radiation Assessment |
| title_short | Hybrid Models for Daily Global Solar Radiation Assessment |
| title_sort | hybrid models for daily global solar radiation assessment |
| topic | Solar radiation, renewable energy, features selection, Forecasting, Artificial Neural Networks. |
| url | https://periodicos.ufv.br/jcec/article/view/15926 |
| work_keys_str_mv | AT abdelkerimsouahlia hybridmodelsfordailyglobalsolarradiationassessment AT abdelhalimrabehi hybridmodelsfordailyglobalsolarradiationassessment AT abdelazizerabehi hybridmodelsfordailyglobalsolarradiationassessment |