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

Noppamas Pratummasoot; Pranomkorn Choosri; Sumaman Buntoung

doi:10.14456/nujst.2020.40

Authors

Noppamas Pratummasoot Applied Physics Program, Faculty of Science and Technology, Valaya Alongkorn Rajabhat University under the Royal Patronage, Pathum Thani 13180, Thailand
Pranomkorn Choosri Department of Biomedical Engineering, College of Heath Science, Christian University of Thailand, Nakhon Pathom 73000, Thailand
Sumaman Buntoung Department of Physics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand

DOI:

https://doi.org/10.14456/nujst.2020.40

Keywords:

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

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 (k_t) 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.

Author Biography

Pranomkorn Choosri, Department of Biomedical Engineering, College of Heath Science, Christian University of Thailand, Nakhon Pathom 73000, Thailand

Department of Biomedical Engineering, College of Heath Science, Christian University of Thailand, Nakhon Pathom 73000, Thailand

References

Cumming, G., Fidler, F. & Vaux, D.L. (2007). Error bars in experimental biology. Journal of Cell Biology, 177, 7-11.

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.