This paper describes an investigation of the effect on electric lighting demand of applying occupancy models of various resolution to climate-based daylight modelling. The lighting demand was evaluated for a building zone with the occupant always present, with occupancy corresponding to absence factors, based on an estimated annual mean occupancy, based on estimated 1-hour mean occupancy, and based on 2-min occupancy intervals. The results showed little difference in the annual electric lighting demand when the same occupancy profile was used every day, as opposed to when profiles were used where occupancy varied every day. Furthermore, the results showed that annual electric lighting demand was evaluated slightly conservatively when a mean absence factor was applied as opposed to using dynamic occupancy profiles.
Get full access to this article
View all access options for this article.
References
1.
European Lamp Companies Federation. Fact sheet: Did you know…the potential energy savings of an energy efficient lamp? Retrieved 10 August 2012, from http://www.elcfed.org/1_health.html.
2.
ManicciaDRutledgeBReaMSMorrowW. Occupant use of manual lighting controls in private offices. Journal of the Illuminating Engineering Society1999; 28: 42–56.
3.
JenningsJDRubinsteinFMDiBartolomeoDBlancSL. Comparison of control options in private offices in an advanced lighting controls testbed. Journal of the Illuminating Engineering Society2000; 29: 39–60.
4.
GalasiuADNewshamGSuvagauCSanderDM. Energy saving lighting control systems for open-plan offices: a field study. Leukos2007; 4: 7–29.
5.
Dansk Standard DS/EN15193. Energy Performance of Buildings - Energy Requirements for Lighting, Chartlottelund: Dansk Standard, 2007.
6.
WangDFederspielCCRubinsteinF. Modeling occupancy in single person offices. Energy and Buildings2005; 37: 121–126.
7.
PageJRobinsonDMorelNScartezziniJL. A generalised stochastic model for the simulation of occupant presence. Energy and Buildings2008; 40: 83–98.
8.
DelffPIversenAMadsenHRodeC. Dynamic modelling of occupancy presence using inhomogeneous Markov Chains. Submitted to Energy and Buildings2012.
9.
TabakVde VriesB. Methods for the prediction of intermediate activities by office occupants. Building and Environment2010; 45: 1355–1372.
10.
RichardsonIThomsonMInfieldD. A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings2008; 40: 1560–1566.
11.
Newsham G, Mahdavi A, Beausoleil-Morrison I. Lightswitch: A stochastic model for predicting office lighting energy consumption: Proceedings of Right Light Three, the 3rd European Conference on Energy Efficient Lighting, Newcastle, UK, 18–21 June, 1995: 60–66.
12.
HuntDRG. The use of artificial lighting in relation to daylight levels and occupancy. Building and Environment1979; 14: 21–33.
13.
ReinhartCF. Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy2004; 77: 15–12.
14.
Bourgeois D. Detailed occupancy prediction, occupancy-sensing control and advanced behavioural modelling within whole-building energy simulation. PhD thesis. Quebec: l'Universite Laval, 2005.
15.
Reinhart CF. Daylight availability and manual lighting control in office buildings - simulation studies and analysis of measurements. PhD thesis. Karlsruhe: University of Karlsruhe, 2001.
16.
Haldi F. Towards a unified model of occupants' behaviour and comfort for building energy simulation. PhD thesis. Lausanne: Ecole Polytechnique Federale Lausanne, 2010.
17.
HoesPHensenJLMLoomasMGLCde Vries BBourgeoisD. User behaviour in whole building simulation. Energy and Buildings2009; 41: 295–302.
18.
DIALux, Version 4.9. The Software Standard for Calculating Lighting Layouts. User Manual, 2011. Retrieved 10 July 2012, from http://www.dial.de.
ReinhartCF. Tutorial on the Use of Daysim Simulations for Sustainable Design, Cambridge, MA: Harvard University Graduate School of Design, 2010.
21.
LarsonGWShakespeareR. Rendering with Radiance - The Art and Science of Lighting Visualization, San Francisco, CA: Morgan Kaufmann, 1998.
22.
WardGMistrickRLeeESMcNeilAJonssonJ. Simulating the daylight performance of complex fenestration systems using bidirectional scattering distribution functions within Radiance. Leukos2011; 7: 241–261.
WalkenhorstOLutherJReinhartCFTimmerJ. Dynamic annual daylight simulations based on one-hour and one-minute means of irradiance data. Solar Energy2002; 72: 385–395.
26.
Iversen A, Svendsen S, Nielsen TR. The effect of different weather data sets and their resolution in climate-based daylight modelling. Lighting Research and Technology 2012. Epub ahead of print 9 March 2012. DOI: 10 1177/1477153512440545.
27.
MardaljevicJHeschongLLeeE. Daylight metrics and energy savings. Lighting Research and Technology2009; 41: 261–283.
28.
GrimmetGStirzakerD. Probability and Random Processes, Oxford: Oxford University Press, 2005.
29.
MadsenHThyregodP. Introduction to General and Generalized Linear Models, London: Chapman & Hall/CRC, 2011.
30.
WenY-JAgoginoAM. Control of wireless-networked lighting in open-plan offices. Lighting Research and Technology2011; 43: 225–248.
31.
WenY-JAgoginoAM. Personalized dynamic design of networked lighting for energy-efficiency in open-plan offices. Energy and Buildings2011; 43: 1919–1924.
