Energy shortage and climate change are the global issues that give effect to many aspect of human society. Finding new green energy, saving and reducing energy consumption are among the solutions. Consequently, green architecture developed in accordance with this trend for the past years. Current building standards (LEED, BREAM…) require many aspect of building performance: sustainable site, water efficiency, energy and atmosphere, indoor environment quality, material and resources… And a very important aspect: lighting.

Better lighting decision can be made from appropriate measures. One early-stage measure is computer-based simulation. Simulation assists architect for creating a virtual reality of their design; allows them to make different study-cases, and later on choose the most suitable one. Thenceforth a question is raised about simulation’s precision: how close is the precision between reality and virtual simulation? By combining computer-based lighting simulation and portable light meters, this study tries to find the relationship between virtual lighting simulation and real context.
The combination described above is expected to be a practical lighting design approach. Main components are:
The 1st: Lighting and material information acquisition
The 2nd: Computer-based representing
The 3rd: Comparison and evaluation
The 1st component is a process of luminance / illuminance and material’s properties acquisition by using portable light meters and HDR technique. HDR technique has ability of producing better luminance image at higher resolution, smooth gradation, in-expensive manner and time saving. Supported by other studies [01, 02, 03, 07], this technique proves practical reliability.
The 2nd component is a procedure from acquiring material lighting fixture’s properties to representing not only 3D geometry but also physical properties in computer-based environment. To achieve better result, portable equipments for properties acquisition are used: spectrophotometer (for color acquisition), ISAAC lighting compass (for lighting intensity distribution acquisition).
The last component is a comparison and evaluation. Calibration between reality and virtuality’s results is conducted at this point. By calibration, the gap between simulation and reality is narrowed, thus greatly affects to lighting decision.

For building, lighting is the second largest single end-user of primary energy, and the largest single end-user of electricity [12]. Reduce lighting consumption will give greatly effect to building’s total energy conservation. The virtual-physical integrated lighting design approach described in this study is expected to enhance the quality of design decision making for architect and lighting designer. Moreover bridge between reality and computer simulation in lighting research.
 

Energy shortage and climate change are the global issues that give effect to many aspect of human society. Finding new green energy, saving and reducing energy consumption are among the solutions. Consequently, green architecture developed in accordance with this trend for the past years. Current building standards (LEED, BREAM…) require many aspect of building performance: sustainable site, water efficiency, energy and atmosphere, indoor environment quality, material and resources… And a very important aspect: lighting.

Better lighting decision can be made from appropriate measures. One early-stage measure is computer-based simulation. Simulation assists architect for creating a virtual reality of their design; allows them to make different study-cases, and later on choose the most suitable one. Thenceforth a question is raised about simulation’s precision: how close is the precision between reality and virtual simulation? By combining computer-based lighting simulation and portable light meters, this study tries to find the relationship between virtual lighting simulation and real context.
The combination described above is expected to be a practical lighting design approach. Main components are:
The 1st: Lighting and material information acquisition
The 2nd: Computer-based representing
The 3rd: Comparison and evaluation
The 1st component is a process of luminance / illuminance and material’s properties acquisition by using portable light meters and HDR technique. HDR technique has ability of producing better luminance image at higher resolution, smooth gradation, in-expensive manner and time saving. Supported by other studies [01, 02, 03, 07], this technique proves practical reliability.
The 2nd component is a procedure from acquiring material lighting fixture’s properties to representing not only 3D geometry but also physical properties in computer-based environment. To achieve better result, portable equipments for properties acquisition are used: spectrophotometer (for color acquisition), ISAAC lighting compass (for lighting intensity distribution acquisition).
The last component is a comparison and evaluation. Calibration between reality and virtuality’s results is conducted at this point. By calibration, the gap between simulation and reality is narrowed, thus greatly affects to lighting decision.

For building, lighting is the second largest single end-user of primary energy, and the largest single end-user of electricity [12]. Reduce lighting consumption will give greatly effect to building’s total energy conservation. The virtual-physical integrated lighting design approach described in this study is expected to enhance the quality of design decision making for architect and lighting designer. Moreover bridge between reality and computer simulation in lighting research.
 

REFERENCES:
01. Mehlika Inanici PhD.
Evaluation of high dynamic range photography as a luminance data acquisition system.
Lawrence Berkeley National Laboratory, Lighting Research Group, Berkeley, California, USA. Lighting Res. Technol. Vol 38 Issue 2 (2006) pp. 123 - 136.
Received 17 January 2005; revised 21 July 2005; accepted 9 August 2005.
02. Mehlika Inanici, Jim Galvin
Evaluation of high dynamic range photography as a lumiance mapping technique.
Lighting research group, Building technologies department, Environmental energy technologies division, Lawrence Berkeley National Laboratory.
December 2004.
03. Laura Bellia, Marilena Musto, Gennaro Spada.
Illuminance measurements through HDR imaging photometry in scholastic environment.
DETEC – University of Naples “Federico II”, P.le Tecchio, 80 - 80125 Naples, Italy
Energy and Buildings Vol 43 (2011) pp. 2843–2849.
Received 19 October 2010. Received in revised form 7 May 2011. Accepted 3 July 2011
04. Mehlika Inanici PhD.
Application of the state-of-the-art computer simulation and visualization in architectural lighting research.
University of Michigan, Taubman College of Architecture & Urban Planning. Ann Arbor, MI, 48105, USA.
Seventh International IBPSA Conference. Rio de Janeiro, Brazil.
August 13-15, 2001.
05. ASJ Bergen B. App.Sci. Hons
A Practical method of comparing luminous intensity distributions
Photometric Solutions International Pty Ltd, Huntingdale, Victoria 3166, Australia.
Lighting Res. Technol. 2012; Vol 44: 27–36
Received 17 November 2011; Revised 5 December 2011; Accepted 22 December 2011
06. M. Bodarta, R. de Penarandab, A. Deneyerc, G. Flamantc
Photometry and colorimetry characterisation of materials in daylighting evaluation tools
a Fonds de la Recherche Scientifique (FNRS), Universite′ Catholique de Louvain, Unite′ d’Architecture, Place du Levant, 1, B-1348 Louvain-La-Neuve, Belgium
b Universite′ Catholique de Louvain, Unite′ d’Architecture, Place du Levant, 1, B-1348 Louvain-La-Neuve, Belgium
c Department of Building Physics and Equipments, Belgian Building Research Institute (BBRI), Avenue Pierre Holoffe, 21, B-1342 Limelette, Belgium
Building and Environment Vol 43 (2008) pp. 2046–2058
Received 3 October 2007; received in revised form 6 December 2007; accepted 9 December 2007
07. H Cai PhD and TM Chung PhD.
Improving the quality of high dynamic range images.
Department of Building Services Engineering, Hong Kong Polytechnic University, Hung Hom,
Kowloon, Hong Kong
Lighting Res. Technol. 2011; 43: 87–102
Received 13 November 2009; Revised 21 March 2010; Accepted 10 April 2010
08. Reinhard E, Ward G, Pattanaik S, Debevec P.
High dynamic range imaging: Acquisition, Display and Image-based lighting.
San Francisco, CA: Morgan Kaufmann, 2005 Second Edition.
09. F Cantin MSc, M Archa and M-C Dubois PhD, M Archb
Daylighting metrics based on illuminance, distribution, glare and directivity
a Hudon and Julien Architects, 1175 Lavigerie, Office 400, Que′ bec, Canada
b School of Architecture, Universite′ Laval, 1 Coˆ te de la Fabrique, Que′ bec, Canada
Lighting Res. Technol. 2011; 43: 291–307
Received 30 August 2010; Revised 19 October 2010; Accepted 16 November 2010.
10. J Mardaljevic BSc MPhil PhD
Verification of program accuracy for illuminace modeling: Assumptions, methodology, and an examination of conflicting findings
Institute of Energy and Sustainable Development (IESD), De Montfort University, Leicester, UK
Lighting Res. Technol. 36,3 (2004) pp. 217–242
Received 30 December 2003; revised 10 April 2004; accepted 13 April 2004
11. Greg Ward
Photosphere (http://anyhere.com/)
12. Lighting A Revolution
(http://americanhistory.si.edu/lighting/tech/)
13. Ray, Sidney F. 2000.
Camera Exposure Determination. In The Manual of Photography: Photographic and Digital Imaging, 9th ed. Ed. Ralph E. Jacobson, Sidney F. Ray, Geoffrey G. Atteridge, and Norman R. Axford. Oxford: Focal Press. ISBN 0-240-51574-9
14. Radiance synthetic imaging system – Eighteenth release – version 4.1
http://radsite.lbl.gov/Radiance
15. Radiance synthetic imaging system – eighteenth release – version 4.1
Reference manual
http://radsite.lbl.gov/Radiance/refer/ray.html
16. Material webtool
http://www-energie.arch.ucl.ac.be/materiaux/defaulte.asp
17. Easy RGB calculator
http://www.easyrgb.com/index.php?X=CALC#Result
18. Python-colormath
http://code.google.com/p/python-colormath/
19. IES file format
http://www.ltblight.com/English.lproj/LTBLhelp/pages/iesformat.html
20. Radiance tutorial
Axel Jacobs. jacobs.axel@gmail.com
21. ColorChecker Passport Technical Preview
Robin D. Myers. 14th October 2009. Revised 5th July 2010.
22. Optimizing the Prediction Accuracy of Concrete Compressive Strength Based on a Comparison of Data-Mining Techniques
Jui-Sheng Chou, P.E., Ph.D.; Chien-Kuo Chiu, P.E., Ph.D.; Mahmoud Farfoura; and Ismail Al-Taharwa
23. Wikipedia – The free encyclopedia.
wikipedia.org