Building Simulation

, Volume 11, Issue 3, pp 483–495 | Cite as

The impact of hourly solar radiation model on building energy analysis in different climatic regions of Turkey

  • Kaan Yaman
  • Gökhan Arslan
Research Article Building Thermal, Lighting, and Acoustics Modeling


The purpose of this study is to investigate the effect of solar radiation models on the determination of energy performance of a single-family house assisted with renewable energy system including photovoltaic panels and solar water heater. An Angström-Prescott type solar radiation model was compared with Zhang and Huang model derived based on hourly meteorological data of 12 locations in Turkey. Since regression coefficients of the Zhang and Huang model are valid for China, new regression coefficients were derived by using local meteorological data. A clear distinction could not be observed in simulated annual heating load intensity for each model since the average relative deviation of the models’ results was 2.5%. However, the average deviation was 12.5% for space cooling load intensity. Primary energy ratings (PER) and the renewable energy ratio (RER) were determined for each location. For total PER, the highest deviation was 4.6% and 3.3% for Mersin and Muğla, respectively. For the other locations, this parameter deviates between 0.02%–2.11%. The highest RER was 18.6% for Mersin.


building energy simulation solar radiation models renewable energy ratio primary energy rating heating load intensity cooling load intensity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Al-Anzi A, Seo D, Krarti M (2008). Impact of solar model selection on building energy analysis for Kuwait. Journal of Solar Energy Engineering, 130(2): 021004.CrossRefGoogle Scholar
  2. Almorox J, Hontoria C, Benito M (2011). Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain). Applied Energy, 88: 1703–1709.CrossRefGoogle Scholar
  3. Becchio C, Dabbene P, Fabrizio E, Monetti V, Filippi M (2015). Cost optimality assessment of a single family house: Building and technical systems solutions for the nZEB target. Energy and Buildings, 90: 173–187.CrossRefGoogle Scholar
  4. Chukwujindu NS (2017). A comprehensive review of empirical models for estimating global solar radiation in Africa. Renewable and Sustainable Energy Reviews, 78: 955–995.CrossRefGoogle Scholar
  5. DOE (2015). EnergyPlus Documentation Input and Output Reference Version 8.4. Washington: US Department of Energy.Google Scholar
  6. Erbs DG, Klein SA, Duffie JA (1982). Estimation of the diffuse radiation fraction for hourly, daily and monthly-average global radiation. Solar Energy, 28: 293–302.CrossRefGoogle Scholar
  7. Finkelstein JM, Schafer RE (1971). Improved goodness of fit tests. Biometrika, 58: 641–645.CrossRefzbMATHGoogle Scholar
  8. Hachem C, Athienitis A, Fazio P (2014). Energy performance enhancement in multistory residential buildings. Applied Energy, 116: 9–19.CrossRefGoogle Scholar
  9. Hall IJ, Prairie RR, Anderson HE, Boes EC (1978). Generation of typical meteorological years for 26 SOLMET stations. Albuquerque: Sandia Lab Report.Google Scholar
  10. Hargreaves GH, Samani ZA (1982). Estimating of potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3): 223–230.Google Scholar
  11. Harish VSKV, Kumar A (2016). A review on modeling and simulation of building energy systems. Renewable and Sustainable Energy Reviews, 56: 1272–1292.CrossRefGoogle Scholar
  12. Huang YJ, Su F, Seo D, Krarti M (2014). Development of 3012 IWEC2 weather files for international locations (RP-1477). ASHRAE Transactions, 120(1): 340–355.Google Scholar
  13. Hunt LA, Kuchar L, Swanton CJ (1998). Estimation of solar radiation for use in crop modelling. Agricultural and Forest Meteorology, 91: 293–300.CrossRefGoogle Scholar
  14. Kambezidis HD, Psiloglou BE (2008). The meteorological radiation model (MRM): Advancements and applications. In: Badescu V (ed), Modeling Solar Radiation at the Earth’s Surface: Recent advances. Berlin: Springer. pp. 357–392.CrossRefGoogle Scholar
  15. Kambezidis HD, Psiloglou BE, Karagiannis D, Dumka UC, Kaskaoutis DG (2017). Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products. Renewable and Sustainable Energy Reviews, 74: 616–637.CrossRefGoogle Scholar
  16. Kasten F, Czeplak G (1980). Solar and terrestrial radiation dependent on the amount and type of cloud. Solar Energy, 24: 177–189.CrossRefGoogle Scholar
  17. Kilic A, Ozturk A (1983). Solar Energy Ankara: Kipas. (in Turkish)Google Scholar
  18. Kim KH, Oh JK, Jeong W (2016). Study on solar radiation models in South Korea for improving office building energy performance analysis. Sustainability, 8: 589–603.CrossRefGoogle Scholar
  19. Krarti M (2003). Overview of artificial intelligence-based methods for building energy systems. Journal of Solar Energy Engineering, 125: 331–342.CrossRefGoogle Scholar
  20. Kurnitski J (2013). Technical definition for nearly zero energy buildings. REHVA European HVAC Journal, 2013(3): 22–28.Google Scholar
  21. Kwak Y, Huh J-H (2016). Development of a method of real-time building energy simulation for efficient predictive control. Energy Conversion and Management, 113: 220–229.CrossRefGoogle Scholar
  22. MENR (2016). The Republic of Turkey, Ministry of Energy and Natural Resources. Available at Accessed 08 Mar 16.Google Scholar
  23. MENR (2017). The Republic of Turkey, Ministry of Energy and Natural Resources. Available at Default.aspx. Accessed 28 Jun 2017.Google Scholar
  24. Meteonorm (2014), Global Database. Handbook Part I.Google Scholar
  25. Muneer T, Gul MS, Kambezidis HD, Allwinkle S (1996). An all-sky solar meteorological radiation model for the UK. In: Proceedings of the Joint CIBSE/ASHRAE Conference, London, UK.Google Scholar
  26. Muneer T, Gul MS, Kambezidis HD (1997). Evaluation of an all-sky meteorological radiation model against long-term measured hourly data. Energy Conversion and Management, 39: 303–317.CrossRefGoogle Scholar
  27. NREL (2008). Users manual for TMY3 data sets, Typical Meteorological Years. Technical Report. Golden, CO, USA: National Renewable Energy Laboratory.Google Scholar
  28. NREL (2017). National Renewable Energy Laboratory. Available at Accessed 20 Feb 17.Google Scholar
  29. Perez R, Ineichen P, Seals R, Zelenka A (1990). Making full use of the clearness index for parameterizing hourly insolation conditions. Solar Energy, 45: 111–114.CrossRefGoogle Scholar
  30. Perez RR, Ineichen P, Maxwell EL, Seals RD, Zelenka A (1992). Dynamic global-to-direct irradiance conversion models. ASHRAE Transactions, 98(1): 354–369.Google Scholar
  31. Ramesh T, Prakash R, Shukla KK (2012). Life cycle approach in evaluating energy performance of residential buildings in Indian context. Energy and Buildings, 54: 259–265.CrossRefGoogle Scholar
  32. SNL (2016). PV_LIB Toolbox for MatLab developed at Sandia National Laboratories. Available at members/pv_lib-toolbox. Accessed 08 Mar 2016.Google Scholar
  33. SNL (2017). Sandia National Labs. Available at Accessed 20 Feb 17.Google Scholar
  34. Seo D, Krarti M (2007). Impact of solar models on building energy analysis for tropical sites. ASHRAE Transactions, 113(1): 523–530.Google Scholar
  35. SRCC (2017). Solar Rating and Certification Corporation. Available at Accessed 20 Feb 17.Google Scholar
  36. Thevenard D, Brunger A (2002). Typical Weather Year for international locations. Final report for ASHRAE Research Project 1015-RP. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  37. Turkish Standards Institution (2013). TS825 Thermal Insulation Requirements for Building. Ankara, Turkish Standards Institution. (in Turkish)Google Scholar
  38. Turkish Statistical Institute (2016). Turkish Standards Institution. Available at Accessed 08 Mar 16. (in Turkish)Google Scholar
  39. Walton GN (1983). Thermal Analysis Research Program (TARP) Reference Manual. NBSSIR 83-2655. Washington, DC, USA: National Bureau of Standards.Google Scholar
  40. Wan KKW, Cheung KL, Liu D, Lam JC (2009). Impact of modelled global solar radiation on simulated building heating and cooling loads. Energy Conversion and Management, 50: 662–667.CrossRefGoogle Scholar
  41. Wang L, Gwilliam J, Jones P (2009). Case study of zero energy house design in UK. Energy and Buildings, 41: 1215–1222.CrossRefGoogle Scholar
  42. 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 of Architectural Institute of Japan, 330: 96–108. (in Japanese)CrossRefGoogle Scholar
  43. Zhang QY, Huang YJ (2002). Development of typical year weather files for chinese locations. ASHRAE Transactions, 108(2): 1063–1075.MathSciNetGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringMersin UniversityMersinTurkey

Personalised recommendations