Comparative study of statistical and artificial neural network's methodologies for deriving global solar radiation from NOAA satellite images

2012 ◽  
Vol 33 (2) ◽  
pp. 480-486 ◽  
Author(s):  
A. Rahimikhoob ◽  
S. M. R. Behbahani ◽  
M. E. Banihabib
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Satellite Images ◽  
Global Solar Radiation ◽  
Noaa Satellite ◽  
Artificial Neural

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Energy ◽  
2011 ◽  
Vol 36 (8) ◽  
pp. 5356-5365 ◽  
Author(s):  
Alvaro Linares-Rodríguez ◽  
José Antonio Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Joaquín Tovar-Pescador
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Radiation Data ◽  
Artificial Neural
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