Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization
作者： F.A. Essa , Mohamed Abd Elaziz , Ammar H. Elsheikh
DOI: 10.1016/J.PSEP.2020.07.044
关键词: Mean squared error 、 Mathematical optimization 、 Multivariate random variable 、 Environmental science 、 Seawater 、 Desalination 、 Seawater greenhouse 、 Coefficient of determination 、 Artificial neural network 、 Approximation error
摘要: Abstract The seawater greenhouse desalination technology is a kind of plant which simulates the water cycle through evaporation and condensation into freshwater. A novel random vector functional link (RVFL) network integrated with artificial ecosystem-based optimization (AEO) algorithm proposed to predict performance (SWGH) system. Power consumption productivity SWGH are predicted using RVFL-AEO model. statistical analyses different criteria such as root mean square error, absolute relative efficiency coefficient, coefficient determination, overall index, coefficient residual mass also carried out examine efficiency neural network. tools obtained perfect match between experimental model results. compared that conventional RVFL showed better RVFL; indicates role AEO in obtaining optimal parameters enhances accuracy
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