Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction
article; artificial neural network; back propagation; conjugate; controlled study; Malaysia; prediction; relative humidity; solar radiation; wind speed; algorithm; Bayes theorem; meteorology; solar energy; Algorithms; Bayes Theorem; Meteorology; Neural Networks, Computer; Solar Energy
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Nature Research
2023
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my.uniten.dspace-266562023-05-29T17:36:04Z Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction Heng S.Y. Ridwan W.M. Kumar P. Ahmed A.N. Fai C.M. Birima A.H. El-Shafie A. 57751621900 57218502036 57206939156 57214837520 57214146115 23466519000 16068189400 article; artificial neural network; back propagation; conjugate; controlled study; Malaysia; prediction; relative humidity; solar radiation; wind speed; algorithm; Bayes theorem; meteorology; solar energy; Algorithms; Bayes Theorem; Meteorology; Neural Networks, Computer; Solar Energy Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg�Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models. � 2022, The Author(s). Final 2023-05-29T09:36:04Z 2023-05-29T09:36:04Z 2022 Article 10.1038/s41598-022-13532-3 2-s2.0-85132267438 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132267438&doi=10.1038%2fs41598-022-13532-3&partnerID=40&md5=0f965de70db10867c6cbf896013d1c18 https://irepository.uniten.edu.my/handle/123456789/26656 12 1 10457 All Open Access, Gold, Green Nature Research Scopus |
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article; artificial neural network; back propagation; conjugate; controlled study; Malaysia; prediction; relative humidity; solar radiation; wind speed; algorithm; Bayes theorem; meteorology; solar energy; Algorithms; Bayes Theorem; Meteorology; Neural Networks, Computer; Solar Energy |
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57751621900 |
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57751621900 Heng S.Y. Ridwan W.M. Kumar P. Ahmed A.N. Fai C.M. Birima A.H. El-Shafie A. |
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Article |
| author |
Heng S.Y. Ridwan W.M. Kumar P. Ahmed A.N. Fai C.M. Birima A.H. El-Shafie A. |
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Heng S.Y. Ridwan W.M. Kumar P. Ahmed A.N. Fai C.M. Birima A.H. El-Shafie A. Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| author_sort |
Heng S.Y. |
| title |
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| title_short |
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| title_full |
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| title_fullStr |
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| title_full_unstemmed |
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| title_sort |
artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction |
| publisher |
Nature Research |
| publishDate |
2023 |
| _version_ |
1806424041755508736 |
| score |
13.222552 |