In this study, Artificial Neural Network (ANN) techniques for estimating daily global UV, PAR and broadband solar radiant fluxes have been developed. The data used in this analysis are global ultraviolet UV (GUV), global photosynthetic photon flux density (PAR-GPAR), broadband global radiant flux (G), extraterrestrial radiant flux E0, air temperature (T), relative humidity (Rh), sunshine duration (n), daylength (N), precipitable water (w) and O3 column density. By using different combinations of the above variables as inputs, numerous ANN-models have been developed. For each model, the output is the daily global UV, PAR and broadband radiant fluxes. Firstly, a set of 2 × 365 points (2 years) has been used for training each network–model, whereas a set of 365 points (1 year) has been engaged for testing and validating the ANN-models. It has been found that ANN-models’ accuracy depends on the parameters used as well as spectral range considered. Moreover, results obtained reveal that the ANN methodology is a promising tool for estimating both broadband and spectral radiant fluxes.
Artificial Neural Network Radiant Flux Sunshine Duration Solar Radiant Flux Precipitable Water Content
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.
Alados I, Mellado JA, Ramos F, Alados-Arboledas L (2004) Estimating UV erythemal irradiance by means of neural networks. Photochem Photobiol 80:351–358CrossRefGoogle Scholar
Barbero FJ, Lopez G, Batlles FJ (2006) Determination of daily solar ultraviolet radiation using statistical models and artificial neural networks. Ann Geophys 24:2105–2114CrossRefGoogle Scholar
Benghanem M, Mellit A, Alamri SN (2009) ANN-based modeling and estimation of daily global solar radiation data: a case study. Energy Convers Manage 50:1644–1655CrossRefGoogle Scholar
Bilbao J, Mateos-Villan D, de Miguel A (2010) Analysis and cloudiness influence on UV total radiation. Int J Climatol 31:451–460Google Scholar
Foyo-Moreno I, Alados I, Olmo FJ, Alados-Arboledas I (2003) The influence of cloudiness on UV global irradiance (295–385 nm). Agric For Meteorol 1(20):101–111CrossRefGoogle Scholar
Ge S, Smith RG, Jacovides CP, Kramer MG, Carruthers RI (2011) Dynamics of photosynthetic photon flux density (PPFD) and estimates in coastal northern California. Theor Appl Climatol 105(1–2):107–118CrossRefGoogle Scholar
Jacovides CP, Kontogianis H (1995) Statistical procedure for the evaluation of evapotranspiration computing models. Agric Water Manage 27:365–371CrossRefGoogle Scholar
Jacovides CP, Tymvios FS, Asimakopoulos DN, Kaltsounides NA (2009) Solar global UVB (280–315 nm) and UVA (315–380 nm) radiant fluxes and their relationships with broadband global radiant flux at an eastern Mediterranean site. Agric For Meteorol 149:1188–1200CrossRefGoogle Scholar
Jacovides CP, Boland J, Rizou D, Kaltsounides NA, Theoharatos GA (2012) School Students participation in monitoring solar radiation components: preliminary results for UVB and UVA solar radiant fluxes. Ren Energy 39:367–374CrossRefGoogle Scholar
Junk J, Feister U, Helbig A (2007) Reconstruction of daily solar UV irradiation from 1893 to 2002 in Potsdam, Germany. Int J Biometeorol 5:505–512CrossRefGoogle Scholar
Lopez G, Rubio MA, Martinez M, Batlles FJ (2001) Estimation of hourly global photosynthetically active radiation using artificial neural network models. Agric For Meteorol 107:279–291CrossRefGoogle Scholar
McCree KJ (1972) Test of current definitions of photosynthetically active radiation against leaf photosynthesis data. Agric Meteorol 10:443–453CrossRefGoogle Scholar
Parisi AV, Turnbull DJ, Turner J (2007) Calculation of cloud modification factors for the horizontal plane eye damaging ultraviolet radiation. Atmos Res 86:278–285CrossRefGoogle Scholar
Ross J, Sulev M (2000) Sources of errors in measurements of PAR. Agric For Meteorol 10:103–125CrossRefGoogle Scholar
Tymvios FS, Jacovides CP, Michaelides SC, Scouteli C (2005) Comparative study of Angstrom’s and artificial neural networks’ methodologies in estimating global solar radiation. Sol Energy 78:752–762CrossRefGoogle Scholar
Tymvios FS, Michaelides SC, Scouteli C (2008) Estimation of surface solar radiation with artificial neural networks. In: Badescu V (ed) Modeling solar radiation at the Earth’s surface: recent advances. Springer, BerlinGoogle Scholar