DATA ANALYSIS AND MODELING OF LIGHTING ENERGY USE IN LARGE OFFICE BUILDINGS

Lighting consumes about 20 to 40% of total electricity use in large office buildings in the U.S. and China. In order to develop better lighting simulation models it is crucial to understand the characteristics of lighting energy use. This paper analyzes the main characteristics of lighting energy use over various time scales, based on the statistical analysis of measured lighting energy use of 17 large office buildings in Beijing and Hong Kong. It was found that the daily 24-hour variations of lighting energy use were mainly driven by the schedule of the building occupants. Outdoor illumination levels have little impact on lighting energy use in large office buildings due to the lack of automatic daylighting controls and relatively small perimeter areas. A stochastic lighting energy use model was developed based on different occupant activities during six time periods throughout a day, and the annual distribution of lighting power across those periods. The model was verified using measured lighting energy use of one selected building. This study demonstrates how statistical analysis and stochastic modeling can be applied to lighting energy use. The developed lighting model can be adopted by building energy modeling programs to improve the simulation accuracy of lighting energy use.

[1]  K. Steemers,et al.  Time-dependent occupant behaviour models of window control in summer , 2008 .

[2]  D.R.G. Hunt,et al.  The use of artificial lighting in relation to daylight levels and occupancy , 1979 .

[3]  Nicolas Morel,et al.  A field investigation of the intermediate light switching by users , 2006 .

[4]  V. K. Maitreya.  Daytime artificial lighting in office buildings in India , 1977 .

[5]  Christoph F. Reinhart,et al.  Lightswitch-2002: a model for manual and automated control of electric lighting and blinds , 2004 .

[6]  Anca D. Galasiu,et al.  Applicability of daylighting computer modeling in real case studies: comparison between measured and simulated daylight availability and lighting consumption , 2002 .

[7]  James A. Love.  Manual switching patterns in private offices , 1998 .

[8]  Jeong Tai Kim,et al.  Effects of occupancy and lighting use patterns on lighting energy consumption , 2012 .

[9]  Christoph F. Reinhart,et al.  Monitoring manual control of electric lighting and blinds , 2003 .

[10]  Danny H.W. Li,et al.  Lighting and energy performance for an office using high frequency dimming controls , 2006 .

[11]  Ewa Wäckelgård,et al.  A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand , 2009 .

[12]  Danny H.W. Li,et al.  Evaluation of lighting performance in office buildings with daylighting controls , 2001 .

[13]  Francis Rubinstein,et al.  Modeling occupancy in single person offices , 2005 .

[14]  Jeong Tai Kim,et al.  A field survey of visual comfort and lighting energy consumption in open plan offices , 2012 .

[15]  L. K. Norford,et al.  Two-to-one discrepancy between measured and predicted performance of a ‘low-energy’ office building: insights from a reconciliation based on the DOE-2 model , 1994 .