Graybox and adaptative dynamic neural network identification models to infer the steady state efficiency of solar thermal collectors starting from the transient condition

Solar Energy

Volumn 84, Issue 6, 2010, Pages 1027-1046

  Roberto, Baccoli ^a   Ubaldo, Carlini ^a   Stefano, Mariotti ^a   Roberto, Innamorati ^a   Elisa, Solinas ^a   Paolo, Mura ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

Author keywords

Artificial neural networks; Graybox model; Solar collector; Solar collector parameters inverse problem; Transient test

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; GRAY-BOX MODELS; GRAYBOX MODEL; TRANSIENT TESTS;

IDENTIFICATION (CONTROL SYSTEMS); INLET FLOW; INVERSE PROBLEMS; PIPE; SOLAR COLLECTORS; SOLAR ENERGY; SOLAR HEATING; SUN; TRANSIENT ANALYSIS;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; ENERGY BALANCE; HEAT TRANSFER; INVERSE PROBLEM; NUMERICAL MODEL; PERFORMANCE ASSESSMENT; PHOTOVOLTAIC SYSTEM; SOLAR POWER; SOLAR RADIATION; STEADY-STATE EQUILIBRIUM;

EID: 77953128164     PISSN: 0038092X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.solener.2010.03.011     Document Type: Article

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