Using artificial neural networks to predict the impact of daylighting on building final electric energy requirements

Energy and Buildings

Volumn 61, Issue , 2013, Pages 31-38

  Fonseca, Raphaela Walger Da ^a,b   Didoné, Evelise Leite ^c   Pereira, Fernando Oscar Ruttkay ^a,d  

^a FEDERAL UNIVERSITY OF SANTA CATARINA   (Brazil)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^d LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

Artificial neural networks; Daylighting; Energy efficiency; Multivariate linear regression

Indexed keywords

APPROXIMATION MODEL; ARTIFICIAL LIGHTING; BETTER PERFORMANCE; COEFFICIENT OF DETERMINATION; GENERALIZATION PROCESS; LIMITED DATA SETS; MULTIVARIATE LINEAR REGRESSIONS; STATISTICAL PERFORMANCE;

COMPUTER SIMULATION; ENERGY EFFICIENCY; NEURAL NETWORKS; OFFICE BUILDINGS; PERSONNEL TRAINING;

DAYLIGHTING;

EID: 84874919995     PISSN: 03787788     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.enbuild.2013.02.009     Document Type: Article

References (30)

1. M.S. Mayhoub, and D.J. Carter The costs and benefits of using daylight guidance to light office buildings Building and Environment 46 3 2011 698 710
2. C.F. Reinhart, and J. Wienold The daylighting dashboard-a simulation-based design analysis for daylit spaces Building and Environment 46 2 2011 386 396
3. M.B.D. Souza, Potentiality of daylighting through use of automatic control for energy saving. Ph.D. Thesis (in Production Engineering) Programa de Pós-Graduação em Engenharia de Produção, Universidade Federal de Santa Catarina, Florianópolis, 2003, p. 208 (in Portuguese).
4. J.C. Carlo, E. Ghisi, and R. Lamberts Energy efficiency building code of Salvador Eighth International IBPSA Conference, Proceedings... Eindhoven, IBPSA 2003
5. E.L. Didoné, The influence of daylighting in energy efficiency evaluation the contemporary Office buildings in Florianpolis/SC. Dissertation (Master in Architecture and Urbanism), Universidade Federal de Santa Catarina, Florianopolis, 2009, p. 174 (in Portuguese).
6. T. Kazanasmaz, M. Günaydin, and S. Binol Artificial neural networks to predict daylight illuminance in office buildings Building an Environment 44 2009 1751 1757
7. D.H.W. Li, and E.K.W. Tsang An analysis of daylighting performance for office buildings in Hong Kong Building and Environment 43 9 2008 1446 1458 (Pubitemid 351699653)
8. L. Doulos, A. Tsangrassoulis, and F. Topalis Quantifying energy savings in daylight responsive systems: the role of dimming electronic ballasts Energy and Buildings 40 1 2008 36 50 (Pubitemid 47625914)
9. F. Rubinstein, and A. Enscoe Saving energy with highly-controlled lighting in an open-plan office LEUKOS-Journal of Illuminating Engineering Society of North America 7 1 2010 21 36
10. D.H.W. Li, J.C. Lam, and S.L. Wong Daylighting and its effects on peak load determination Energy 30 2005 1817 1831
11. F. Westphal, and R. Lamberts Regression analysis of electric energy consumption of commercial buildings in Brazil Proceedings... Building Simulation 2007
12. J.C. Lam, and K.K.W. Wan Multiple regression models for energy use in air-conditioned office buildings in different climates Energy Conversion and Management 51 2010 2692 2697
13. W. Chung, Y.V. Hui, and Y.M. Lam Benchmarking the energy efficiency of commercial buildings Applied Energy 83 2006 1 14 (Pubitemid 41291106)
14. R. Signor, Regression analysis of energy consumption compared to architectural variables for air-conditioned commercial buildings in 14 Brazilian capitals. Dissertation (Master in Civil Engineering). Curso de Pós-graduação em Engenharia Civil, Universidade Federal de Santa Catarina, 1999, p. 122 (in Portuguese).
15. Brazil Law n. 10295, October 17, 2001. Provides the national policy for conservation and rational use of energy. Lex: Diário Oficial da União, Brasília, 2001. Available from: http://www.inmetro.gov.br/ qualidade/lei10295.pdf (accessed 20.03.08) (in Portuguese).
16. J.C. Carlo, Development of methodology for evaluating the envelop energy efficiency of non-residential buildings. Ph.D. Thesis (in Civil Engineering)-Centro Tecnológico, Universidade Federal de Santa Catarina, Florianopolis, 2008 (in Portuguese).
17. K. Kavaklioglu, H. Ceylan, H.K. Ozturk, and O.E. Canyurt Modeling and prediction of Turkey's electricity consumption using artificial neural networks Energy Conversion and Management 50 11 2009 2719 2727
18. S.L. Wong, Wan, K.W. Kevin, Lam, and N.T. Tony Artificial neural networks for energy analysis of office buildings with daylighting Applied Energy 87 2 2010 551 557
19. B.B. Ekici, and U.T. Aksoy Prediction of building energy consumption by using artificial neural networks Advances in Engineering Software 40 5 2009 356 362
20. G.C.F. Costa, An energy consumption evaluation conserning transport in the state of Sao Paulo cities. Dissertation (Master in Civil Engineering). Department of Civil Engineering, University of Sao Paulo, Sao Carlos, 2001, p. 103 (in Portuguese).
21. A.H. Neto, and F.A.S. Fiorelli Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption Energy and Buildings 40 12 2008 2169 2176
22. S.A. Kalogirou, and M. Bojic Artificial neural networks for the prediction of the energy consumption of a passive solar building Energy 25 5 2000 479 491 (Pubitemid 30586361)
23. S. Haykin Neural Networks: Principles and Practices 2001 Publisher Artmed SA New York p. 900
24. P.R. Pizarro, and L.C.L. Souza Indoors daylighting qualification-neural networks and 3DSkyView application Revista do Ambiente Construído 7 1 2007 83 96 January/March 2007 (in Portuguese)
25. M.V. Santana, Influence of construction parameters for energy consumption in office buildings located in Florianopolis-SC. Dissertation (Master in Civil Engineering) Centro Tecnológico, Universidade Federal de Santa Catarina, Florianopolis, 2006 (in Portuguese).
26. ABNT. NBR-5413 Interior illuminance. Associação Brasileira de Normas Técnicas. Rio de Janeiro, 1992, p. 13 (in Portuguese).
27. R. Perez Modeling daylight availability and irradiance components from direct and global irradiance Solar Energy 44 5 1990 271 289 (Pubitemid 21672226)
28. ® design 2009 and daysim 3.0 LEUKOS-Journal of Illuminating Engineering Society of North America 6 1 2009 7 35
29. SPSS SPSS Base 16.0 User's Guide 2007 SPSS Inc. Chicago
30. P. Refaeilzadeh, L. Tang, and H. Liu Cross-Validation 2008 Public.Asu-Arizona State University pp. 1-6