RT info:eu-repo/semantics/article
T1 Modelling of a flat-plate solar collector using artificial neural networks for different working fluid (water) flow rates
A1 Diez, F.J.
A1 Navas Gracia, Luis Manuel
A1 Martínez-Rodríguez, A.
A1 Correa Guimaraes, Adriana
A1 Chico Santamarta, Leticia
AB The operation of a flat-plate solar collector using three different working fluid flows (water, i.e. 1, 1.6, 2 L/min) has been modelled using the artificial neural networks (ANNs) of computational intelligence technique. The ANNs model has been built at the entrance to predict the outlet temperature in the flat-plate solar collector using measured data of solar irradiance, ambient temperature, inlet temperature and working fluid flow. The results obtained conclude the method is accurate with the three flow rates of the working fluid (water) (e.g. RMSE = 0.1781 °C and R2 = 0.9991 for an ANN prediction of the outlet temperature of the working fluid with 2 L/min test and RMSE = 0.0090 [0,1] and R2 = 0.7443 as a performance prediction test of 1 L/min), flexible when choosing the variables used and easy to apply to any solar collector. The Hottel-Whillier-Bliss (HWB) and the international standard ISO 9806 solar collector models are also described and applied using the data obtained in the tests performed on the flat-plate solar collector. The deviation that occurs with the three different flows of the working fluid (water) used, have been verified and also their repercussion when they are applied in the f-chart method.
PB Elsevier BV
SN 0038-092X
YR 2019
FD 2019-08
LK https://uvadoc.uva.es/handle/10324/65657
UL https://uvadoc.uva.es/handle/10324/65657
LA spa
NO Solar Energy, agosto 2019, vol. 188, p. 1320-1331
NO Producción Científica
DS UVaDOC
RD 11-ene-2025