Heng, Seah Yi, Ridwan, Wanie M, Kumar, Pavitra 
ORCID: 0000-0002-4683-724X, Ahmed, Ali Najah, Fai, Chow Ming, Birima, Ahmed Hussein and El-Shafie, Ahmed
(2022)
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction.
SCIENTIFIC REPORTS, 12 (1).
10457-.
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Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation predic.pdf - Published version Download (1MB) | Preview |
Abstract
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayes Theorem, Solar Energy, Algorithms, Meteorology, Neural Networks, Computer |
| Divisions: | Faculty of Science and Engineering > School of Environmental Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 22 Jul 2022 14:24 |
| Last Modified: | 18 Jan 2023 20:55 |
| DOI: | 10.1038/s41598-022-13532-3 |
| Open Access URL: | https://www.nature.com/articles/s41598-022-13532-3 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3159184 |
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