Estimation of Hourly Near Infrared Radiation Using Artificial Neural Network and Performance Comparison with the Semi-Empirical Model at Nakhon Pathom Province

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Noppamas Pratummasoot Pranomkorn Choosri Sumaman Buntoung

Abstract

        In this research, methods for estimating near infrared radiation (NIR: 0.695–2.8 micron) at Silpakorn University, Nakhon Pathom province (13.82◦N, 100.04◦E) have been developed using an artificial neural network (ANN) and a semi-empirical model. The input data of these models consist of aerosol optical depth (AOD) and precipitable water (W) measured by a Sunphotometer, and clearness index (kt) obtained from ratio of measured incident solar radiation to calculated extraterrestrial solar radiation. The ANN and semi-empirical models were formulated using the collected data at Nakhon Pathom station for the period of 2009-2015. Then, the results obtained from these models were tested and validated against the measured data at the station during a two-year-period (2016-2017). The comparison results show that the near infrared radiation obtained from the ANN and semi-empirical models are in reasonable agreement with the measurement. The root mean square difference (RMSD) are 6.08% and 4.47%, and the mean bias difference (MBD) are 4.91% and 3.02% for the ANN and semi-empirical models, respectively.


Keywords: near infrared radiation, artificial neural network, semi-empirical model, Nakhon Pathom

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Research Articles

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How to Cite
PRATUMMASOOT, Noppamas; CHOOSRI, Pranomkorn; BUNTOUNG, Sumaman. Estimation of Hourly Near Infrared Radiation Using Artificial Neural Network and Performance Comparison with the Semi-Empirical Model at Nakhon Pathom Province. Naresuan University Journal: Science and Technology (NUJST), [S.l.], v. 28, n. 4, p. 102-111, june 2020. ISSN 2539-553X. Available at: <http://www.journal.nu.ac.th/NUJST/article/view/Vol-28-No-4-2020-102-111>. Date accessed: 26 feb. 2021. doi: https://doi.org/10.14456/nujst.2020.40.