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
Dougherty, C. (2002). Introduction to Econometrics. England: Oxford University Press.
Duffie, J. A., & Beckman, W. A. (1991). Solar Engineering of Thermal Processes. New York: John Wiley & Sons.
Escobed, J. F., Gome, E. N., Oliverira, A. P., & Soares, J. (2009). Modeling hourly and daily fraction of UV, PAR and NIR to global solar radiaiton under various sky conditions at Botucatu, Brazil. Applied Energy, 86, 299-309.
Escobed, J. F., Gome, E. N., Oliverira, A. P., & Soares, J. (2011). Ratios of UV, PAR and NIR components to global solar radiation measured at Botucatu site in Brazil. Renewable Energy, 36, 169-178.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Applying the Artificial Neural Network to Estimate the Drag Force for an Autonomous Underwater Vehicle. Open Journal of Fluid Dynamics, 4(3), 334-346.
Iqbal, M. (1983). An Introduction to Solar Radiation. New York: Academic Press.
Iqbal, M. (2006). An Introduction to Solar Radiation. New York: Academic Press.
Jacovides, C. P., Tymvios, F. S., Boland, J., & Tsitouri, M. (2015). Artificial Neural Network models for estimating daily solar global UV, PAR and broadband radiant fluxes in an eastern Mediterranean site. Atmospheric Research, 152, 138-145.
Lang, K. R. (2006). Sun, Earth and Sky. New York: Springer.
Lokupitiya, E., Denning, S., Paustian, K., Baker, I., Schaefer, K., Verma, S., … Fischer, M. (2009). Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands. Biogeosciences, 6, 969-986.
Morel, J., Begue, A., Todoroff, P., Martine, J. F., Lebourgeois, V., & Petit, M. (2014). Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation. European Journal of Agronomy, 61, 60-68.
Petty, G. W. (2004). A First Course in Atmospheric Radiation. Madsion, Wisconsin: Sundog Publishing.
Seber, G. A. F., & Wild, C. J. (1989). “Nonlinear Regression”. New York, NY, USA: John Wiley & Sons.
Yusuf, K., Mustafa, K., & Emrah, D. (2017). Biogas engine performance estimation using ANN. Engineering Science and Technology, an International Journal, 20, 1563–1570.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.