DOI QR코드

DOI QR Code

The Effect of Direct and Diffuse Split Models on Building Energy Simulation

직달 산란 분리모델이 건물 에너지 예측에 미치는 영향

  • 이하영 (성균관대 건설환경시스템공학과 대학원) ;
  • 윤성환 (성균관대 건설환경시스템공학과 대학원) ;
  • 박철수 (성균관대 건축토목공학부)
  • Received : 2015.08.27
  • Accepted : 2015.11.21
  • Published : 2015.11.30

Abstract

Weather data are indispensable for building energy simulation. Most weather stations measure only global solar radiation and thus, the global radiation is usually divided into direct and diffuse radiation based on solar models. The solar models being currently used are expressed in regression equations. Several studies have reported that the difference between measured direct/diffuse solar radiation and calculated direct/diffuse solar radiation out of the solar models is not negligible. This study aims to quantify the impact of the direct and diffuse split solar models on energy performance simulation. For this study, three popular solar models were chosen based on the literature review. And, a number of office buildings were simulated while changing several inputs relevant to solar load (e.g. SHGC, window-to-wall ratio, etc.). The sampling cases were made using LHS (Latin Hypercube Sampling), one of Monte Carlo techniques, and an energy simulation tool, EnergyPlus, was used. There is a significant difference between the measured weather data and the values of calculated direct and diffuse solar radiation. However, the difference between energy prediction by the measured weather data and energy prediction by solar models is not significant for large buildings. For small buildings, the difference in energy prediction is not negligible.

Keywords

Acknowledgement

Supported by : 국토교통부

References

  1. ASHRAE. (2002). Guideline 14-2002, Measurement of Energy and Demand Savings. American Society of Heating, Ventilating, and Air Conditioning Engineers, Atlanta, Georgia.
  2. ASHRAE. (2013). ASHRAE Handbook 2013 - Fundamentals, American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc, 2009
  3. Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., & Crawley, D. (2011). US Department of Energy commercial reference building models of the national building stock.
  4. DOE, U. (2010). EnergyPlus Documentation., US Department of Energy.
  5. Dutton, S. & Shao, L. (2008). The assessment of the accuracy of diffuse irradiation models and their potential impact on building simulation.
  6. Hensen, J. L. & Lamberts, R. (Eds.). (2012). Building performance simulation for design and operation. Routledge.
  7. Lanini, F. (2010). Division of global radiation into direct radiation and diffuse radiation, Master's thesis, University of Bern.
  8. NREL. (2014). National Solar Radiation Data Base, http://rredc.nrel.gov/solar/old_data/nsrdb/
  9. Maxwell, E. L. (1987). A quasi-physical model for converting hourly global horizontal to direct normal insolation (No. SERI/TR-215-3087). Solar Energy Research Inst., Golden, CO (USA).
  10. Perez, P., Ineichen, E. Maxwell, Seals, R., & Zelenka. A. (1991). Dynamic global-to-direct irradiance conversion models. ISES Solar World Congress, Denver USA, 951-956
  11. Seo, D. (2010). Development of a universal model for predicting hourly solar radiation-Application: Evaluation of an optimal daylighting controller, Ph.D. thesis, University of Colorado at Boulder
  12. Sin, M. C. (2010). Basic statistics for business and econ omics, Changminsa.
  13. Skartveit, A., Holseth, J. A., & Tuft, M. E. (1998). An hourly diffuse fraction model with correction for variability and surface albedo. Solar Energy, 63(3), 173-183 https://doi.org/10.1016/S0038-092X(98)00067-X
  14. Watanabe, T., Urano, Y., & Hayashi, T. (1983). Procedures for separating direct and diffuse insolation on a horizontal surface and prediction of insolation on tilted surfaces. Transactions, No.330, Architectural Institute of Japan, Tokyo, Japan, 96-108
  15. Wilcox, S. (2012). National Solar Radiation Database 1991-2010 Update: User's Manual. No. NREL/TP-5500-54824. National Renewable Energy Laboratory (NREL), Golden, CO.
  16. Wyss, G. D. & Jorgensen, K. H. (1998). A User's Guide to LHS: Sndia's Latin Hypercube Sampling Software, Albuquerque, NM, Sandia National Laboratories.
  17. Yoon, S. H. & Park, C. S. (2014). Energy performance assessment of existing buildings using Data Envelopment Analysis (DEA). Architectural Institute of Korea, 34(1), 191-192.