A Systematic Review of Neural Network Autoregressive Model with Exogenous Input for Solar Radiation Prediction Modelling Development

• Published in: ASM Science Journal
• Main Author: Mohd M.R.S.; Johari J.; Ruslan F.A.
• Format: Article
• Language: English
• Published: Akademi Sains Malaysia 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85178228141&doi=10.32802%2fASMSCJ.2023.1139&partnerID=40&md5=1e14994a8e7368fb3d66f5443fa827f4

Neural Network is one of the Machine Learning methods that has been applied in various Artificial Intelligence system development including solar radiation prediction modelling. Since there are multiple approaches had been developed using the Neural Network method, the study has been focusing on the development of a Multi-layer Neural Network model that can handle non-linearities and highly dynamic data. The integration of the Multi-layer Neural Network and the Non-linear Autoregressive Model with Exogenous Input (NARX) developed a compromising non-linear Neural Network model which can be applied in the modelling of solar radiation. This paper develops a systematic review of the Neural Network Autoregressive Model with Exogenous Input (NNARX) for solar radiation prediction modelling starts from the architecture and the comparative selection for the Training Function. The model is developed and analysed using MATLAB R2019a software. Results showed that the Levenberg-Marquardt Training Function performed better with the R2 value of 0.94 for training and 0.91 for testing, making it the most suitable for the NNARX in the development of solar radiation prediction modelling. © (2023), (Akademi Sains Malaysia). All Rights Reserved.