Modeling water vapor impacts on the solar irradiance reaching the receiver of a solar tower plant by means of artificial neural networks
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URI: http://hdl.handle.net/10835/15677
ISSN: 0038-092X
DOI: https://doi.org/10.1016/j.solener.2018.04.023
ISSN: 0038-092X
DOI: https://doi.org/10.1016/j.solener.2018.04.023
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López Rodríguez, Gabriel; Gueymard, Christian A.; Bosch Saldaña, Juan Luis; Rapp Arrarás, Igor; Alonso Montesinos, Joaquín Blas; [et al.]Date
2018-04-19Abstract
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...
Palabra/s clave
solar power towers
transmission losses
water vapor
artificial neural networks