Neural representation of a solar collector with statistical optimization of the training set

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3029, Issue , 2004, Pages 87-96

  Zárate, Luis E ^a   Pereira, Elizabeth Marques Duarte ^a   Silva, João Paulo Domingos ^a   Vimeiro, Renato ^a   Diniz, Antônia Sônia Cardoso ^a  

^a PONTIFICAL CATHOLIC UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; NATURAL RESOURCES; NONLINEAR EQUATIONS; OPTIMIZATION; PARAMETER ESTIMATION; PROBLEM SOLVING; SOLAR COLLECTORS; STATISTICAL METHODS; ARTIFICIAL INTELLIGENCE; EXPERT SYSTEMS; NEURAL NETWORKS;

ENERGY PRODUCTION; NEURAL REPRESENTATION; STATISTICAL OPTIMIZATION; TRAINING SETS; NEURAL REPRESENTATIONS; NONLINEAR PROBLEMS;

NEURAL NETWORKS; NONLINEAR EQUATIONS;

EID: 9444286473     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24677-0_10     Document Type: Conference Paper

References (10)

1. G.L. Morrison, and D.B. J. Ranatunga, Transient response of thermosiphon solar collectors. Solar Energy 24, 1980, 191.
2. B.J. Huang, Similarity theory of solar water heater with natural circulation. Solar Energy 25, 1984, 105.
3. A.I. Kudish, P. Santaura, and P. Beaufort, Direct measurement and analysis of thermosiphon flow. Solar Energy, Vol. 35, N° 2, 167-173
4. S.A. Kalogirou, Thermosiphon solar domestic water heating systems: long term performance prediction using ANN, Solar Energy, Vol. 69, N°2, 2000, 167-174.
5. S.A. Kalogirou, S. Panteliou, A. Dentsoras, Modeling solar domestic water heating systems using ANN, Solar Energy, Vol. 68, N° 6, 1999, 335-342.
6. J.A. Duffle, and W.A. Beckman, Solar Engineering of Thermal Processes, (2a. Ed. John Wyley&Sons, Inc., USA 1999).
7. Z.L. Kov́cs, "Redes Neurais Artificiais", (Edi̧cão acadêmica São Paulo, Cap 5, pp.75-76. São Paulo. Brasil, 1996).
8. F. R. Bittencout and L.E. Zárate, Controle da Laminação em Redes Neurais, com Capacidade de Generalização, e Lógica Nebulosa via Fatores de Sensibilidade, CBA, Natal, RN, 2002.
9. Ashrae 93-86 Ra 91. Methods of Testing to Determine the Thermal Performance of Solar Collectors, American Society of Heating, Refrigeration, and Air-Conditioning Engineers, Inc., Atlanta (1986).
10. Zárate, L. E., Pereira, E. M., Silva, J. P., Vimieiro R., Diniz, A. S., & Pires, S. 2003a. Representation of a solar collector via artificial neural networks. In Hamza, M. H. ed. International Conference On Artificial Intelligence And Applications, Benalmádena, Spain, 8-11 September 2003. IASTED: ACTA Press, pp. 517-522.