Heng, Seah Yi and Ridwan, Wanie M. and Kumar, Pavitra and Ahmed, Ali Najah and Fai, Chow Ming and 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). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-13532-3.
Full text not available from this repository.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 |
|---|---|
| Funders: | Monash University |
| Uncontrolled Keywords: | Artificial neural network; Solar radiation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 19 Oct 2023 07:37 |
| Last Modified: | 19 Oct 2023 07:37 |
| URI: | http://eprints.um.edu.my/id/eprint/41909 |
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