An empirical validation of lighting energy consumption using the integrated simulation method

Abstract The objective of this study is to evaluate the predictive accuracy of lighting energy consumption carried out by the EnergyPlus program and by the integrated simulation method (ISM), using the Daysim program. EnergyPlus calculates the interior illuminance based on the split-flux and radiosity method, and overestimates the interior illuminance, and we can see large differences in the EnergyPlus interior illuminance results. MBE by the split-flux method was found to range between 81.5% and 463.4%, and the largest MBE occurred at the deepest point. The Daysim program calculates the interior illuminance based on the ray-tracing method, and the largest MBE is −18.9%, at the middle point of the room. Lighting energy consumption differences are caused by the interior illuminance calculation algorithms in the simulation programs. As a result, the lighting energy consumption derived by the EnergyPlus program without ISM is approximately 34.6% smaller, than that of real consumption. The ISM was improved in the prediction accuracy of lighting energy consumption by 24.6% in absolute value. The results of the lighting energy consumption with ISM are relatively more accurate than the EnergyPlus results without ISM, because the modified lighting schedule is similar to the actual situation.

[1]  L. Alados-Arboledas,et al.  On shadowband correction methods for diffuse irradiance measurements , 1995 .

[2]  J. Michalsky,et al.  A simple procedure for correcting shadowband data for all sky conditions , 1990 .

[3]  Y. Koga,et al.  Models of sky radiance distribution and sky luminance distribution , 2004 .

[4]  F. C Winkelmann.  Daylighting Calculation in DOE-2 , 1982 .

[5]  Tariq Muneer,et al.  A new method for correcting shadow band diffuse irradiance data , 2002 .

[6]  F. Kasten,et al.  A new table and approximation formula for the relative optial air mass , 1964 .

[7]  André Mermoud,et al.  Study of the corrective factor involved when measuring the diffuse solar radiation by use of the ring method , 1983 .

[8]  Tariq Muneer,et al.  Assessment of four shadow band correction models using beam normal irradiance data from the United Kingdom and Israel , 2004 .

[9]  Ana Paula Melo,et al.  IMPACT OF DIFFERENT DAYLIGHTING SIMULATION RESULTS ON THE PREDICTION OF TOTAL ENERGY CONSUMPTION , 2010 .

[10]  Stephen Selkowitz,et al.  Daylighting simulation in the DOE-2 building energy analysis program , 1985 .

[11]  J. Michalsky,et al.  Modeling daylight availability and irradiance components from direct and global irradiance , 1990 .

[12]  J. Orgill,et al.  Correlation equation for hourly diffuse radiation on a horizontal surface , 1976 .

[13]  Karsten Menzel,et al.  Assessing the performance of naturally day-lit buildings using data mining , 2011, Adv. Eng. Informatics.

[14]  Christoph F. Reinhart,et al.  Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds , 2001 .

[15]  Harry Boyer,et al.  A simple evaluation of global and diffuse luminous efficacy for all sky conditions in tropical and humid climate , 2012, 1212.5259.

[16]  Richard Perez,et al.  Luminous efficacy of direct irradiance: Variations with insolation and moisture conditions , 1989 .

[17]  Avraham I. Kudish,et al.  The assessment of four different correction models applied to the diffuse radiation measured with a shadow ring using global and normal beam radiation measurements for Beer Sheva, Israel , 2008 .

[18]  Clayton H. Reitan.  Surface Dew Point and Water Vapor Aloft , 1963 .

[19]  A. Drummond.  On the measurement of sky radiation , 1956 .