Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran

2016 ◽  
Vol 53 ◽  
pp. 1570-1579 ◽  
Author(s):  
Kasra Mohammadi ◽  
Shahaboddin Shamshirband ◽  
Dalibor Petković ◽  
Hossein Khorasanizadeh
2019 ◽  
Vol 43 (1) ◽  
pp. 80-94 ◽  
Author(s):  
Yao Feng ◽  
Dongmei Chen ◽  
Xinyi Zhao

Precise knowledge of direct and diffuse solar radiation is important for energy utilization and agricultural activities. However, field measurements in most areas of the world are only for total solar radiation. The satellite-retrieved direct and diffuse solar radiation show poor performance under overcast skies. Therefore, better empirical models are needed to estimate direct and diffuse solar radiation by considering the impact of aerosols over polluted regions. A case study is conducted in North China with the ground-measured solar radiation and satellite-retrieved aerosol optical depth to improve new empirical models at monthly (from 2000 to 2016) and daily (from 2006 to 2009) level. The improved empirical models are validated using the field measurements and compared with the existing models. Results suggest that these models perform well in estimating direct solar radiation at monthly ( R2 = 0.86–0.91, RMSE = 0.76–0.83 MJ/m2) and daily ( R2 = 0.91–0.94, RMSE = 1.51–1.64 MJ/m2) level. The accuracy of estimated monthly ( R2 = 0.95–0.96, RMSE = 0.57–0.65 MJ/m2) and daily ( R2 = 0.91–0.93, RMSE = 1.09–1.15 MJ/m2) diffuse solar radiation, particularly the maximum diffuse solar radiation value, has been improved compared to the existing models. The models presented in this study can be useful in the improvement and evaluation of solar radiation dataset over polluted regions similar to North China.


2014 ◽  
Vol 81 ◽  
pp. 211-219 ◽  
Author(s):  
Taqiy Eddine Boukelia ◽  
Mohamed-Salah Mecibah ◽  
Imad Eddine Meriche

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