RT Journal Article
T1 Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks
A1 López Rodríguez, Gabriel
A1 Gueymard, Christian A.
A1 Bosh, Juan Luis
A1 Rapp Arrarás, Ígor
A1 Alonso-Montesinos, Joaquín
A1 Pulido Calvo, Inmaculada
A1 Ballestrín, Jesús
A1 Polo, Jesús
A1 Barbero, Javier
AB This work analyses the influence of water vapor on the atmospheric transmission loss of solar radiation between heliostats and the receiver of solar power tower plants. To this purpose, an atmospheric transmission code (MODTRAN) is used to generate values of direct normal irradiance (DNI) reaching the mirror and the receiver under different geometries (including sun position, tower height, and mirror-to-receiver slant range) and atmospheric conditions related to water vapor and aerosols.  These variables are then used as inputs to an artificial neural network (ANN), which is trained to calculate the corresponding DNI attenuation. Two different aerosol scenarios are simulated: an ideal aerosol-free atmosphere, and a widely different one corresponding to semi-hazy conditions. The developed ANN model is then able to provide the DNI attenuation over a wide range of the input variables considered here, with root mean square differences of only 0.8%. The transmission loss due to water vapor is found to decrease with sun elevation. This is explained by the saturation effect in the incident irradiance at the mirror. The simplicity and accuracy of the algorithm are its great strengths, allowing its anticipated inclusion into the actual energy simulation codes currently used for solar tower plant design.
PB Elsevier
SN 0038-092X
SN 1471-1257 (electrónico)
YR 2018
FD 2018-07-15
LK https://hdl.handle.net/10272/22810
UL https://hdl.handle.net/10272/22810
LA eng
NO Gabriel López, Christian A. Gueymard, Juan Luis Bosch, Igor Rapp-Arrarás, Joaquín Alonso-Montesinos, Inmaculada Pulido-Calvo, Jesús Ballestrín, Jesús Polo, Javier Barbero. 2018. Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks. Solar Energy 169, 34-39. DOI: 10.1016/j.solener.2018.04.023
NO The authors are grateful for the financial support provided by Spanish Project PRESOL “Forecast of solar radiation at the receiver of a solar power tower” with references ‘ENE2014-59454-C3-1-R, ENE2014-59454-C3-2-R and ENE2014-59454-C3-3-R’, which is funded by the Ministerio de Economía y Competitividad (Spain) and co-financed by the European Regional Development Fund.
DS Repositorio Institucional de la Universidad de Huelva
RD 31 may 2026