TY - GEN
T1 - An artificial neural network based approach for estimating direct normal, diffuse horizontal and global horizontal irradiances using satellite images
AU - Eissa, Yehia
AU - Marpu, Prashanth
AU - Ghedira, Mohamed Hosni
AU - Ouarda, Taha B.M.J.
AU - Chiesa, Matteo
PY - 2012
Y1 - 2012
N2 - This study proposes the use of an artificial neural network approach to estimate the direct normal irradiance (DNI), diffuse horizontal irradiance (DHI) and global horizontal irradiance (GHI) at temporal and spatial resolutions of 15min and 3km, respectively. Inputs to the models are six thermal channels of the SEVIRI instrument, onboard Meteosat Second Generation, along with solar zenith angle, latitude, longitude, solar time, day number and eccentricity correction. The study will show the generalization of the results when using an ensemble approach as opposed to a single network. For all sky conditions the testing dataset for DNI estimations have relative root mean square error (rRMSE) and relative mean bias error (rMBE) values of 17.8% and -3%, respectively. Results for DHI estimations are 13.4% and +1.6%, respectively, and finally GHI estimation results show error values of 7.3% and -1.7%, respectively.
AB - This study proposes the use of an artificial neural network approach to estimate the direct normal irradiance (DNI), diffuse horizontal irradiance (DHI) and global horizontal irradiance (GHI) at temporal and spatial resolutions of 15min and 3km, respectively. Inputs to the models are six thermal channels of the SEVIRI instrument, onboard Meteosat Second Generation, along with solar zenith angle, latitude, longitude, solar time, day number and eccentricity correction. The study will show the generalization of the results when using an ensemble approach as opposed to a single network. For all sky conditions the testing dataset for DNI estimations have relative root mean square error (rRMSE) and relative mean bias error (rMBE) values of 17.8% and -3%, respectively. Results for DHI estimations are 13.4% and +1.6%, respectively, and finally GHI estimation results show error values of 7.3% and -1.7%, respectively.
KW - Neural networks
KW - Satellite images
KW - Solar maps
KW - Solar resource assessment
UR - https://www.scopus.com/pages/publications/84871606123
M3 - Conference contribution
AN - SCOPUS:84871606123
SN - 9781622760923
T3 - World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conferen
SP - 1897
EP - 1904
BT - World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conference
T2 - World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conference
Y2 - 13 May 2012 through 17 May 2012
ER -