Optimization of windows in order to enhance daylight and thermal performance based on genetic algorithm Case study: A residential building with a common plan in Tabriz, Iran

محورهای موضوعی : Space Ontology International Journal

Farhad Ahmadnejad ¹ , Niloofar Mollayi ² , Fatemeh Mostajer Haghighi ³ , Hasti ValiollahPour ⁴ , Reyhaneh Ghadiri ⁵

1 - Faculty of Architecture and Urbanism, Tabriz Islamic Art University, Tabriz, Iran
2 - Master Degree Student at Architecture and Urbanism Faculty, Tabriz Islamic Art University, Tabriz, Iran
3 - Master Degree Student at Architecture and Urbanism Faculty, Tabriz Islamic Art University, Tabriz, Iran
4 - Master Degree Student at Architecture and Urbanism Faculty, Tabriz Islamic Art University, Tabriz, Iran
5 - Master Degree Student at Architecture and Urbanism Faculty, Tabriz Islamic Art University, Tabriz, Iran

تاریخ دریافت : 1400/11/11 تاریخ پذیرش : 1401/08/10 تاریخ انتشار : 1401/06/10

کلید واژه: Genetic Algorithm, Thermal Comfort, Window, Daylight, Residential building,

چکیده مقاله :

In residential buildings, good daylight is critical in maintaining key aspects of our psychological and physiological health, and windows have a key role in the daylight performance. However, achieving successful window design in the term of daylight is rather problematic, it can cause thermal discomfort in summer or winter. The balancing of different interrelated window factors is particularly challenging for Tabriz climate. Fortunately, because of advancements in building optimization methods in recent years, genetic algorithms used to explore for design solutions have shown their efficiency in solving complicated architectural problems. The aim of this current study is to determine the applicability of a genetic algorithm for the optimization of windows for a typical residential building in the cold climate of Tabriz considering daylight and thermal performance. Using a parametric algorithm and evolutionary multi-objective optimization via Wallacei X plug-in for Grasshopper, various windows-to-wall ratios and sill height were combined, to find potential solutions that achieve a good performance in terms of thermal comfort and daylight. The survey has shown that in optimal conditions, the increase in useful daylight illuminance towards the base cases for south and north facade is 3.2% - 10.3% and the reduction rate of discomfort hours is 1.1%-23.8% through modification of window-to-wall ratios and still height in a residential building with a common plan in this climate.The results illustrated how an optimization methodology can be applied in the early stages of building design to understand how the windows can be tailored to ensure a good balance between daylight and thermal performance.

منابع و مأخذ:

1. Aamer, H. S. M. S. (2021). Bio-Form Mimicry in Architectural Design. Faculty of Engineering at Shoubra, Benha University,


2. Ahadi, A., Masoudinejad, M., & Piriaei, A. (2016). Correct design of windows in order to achieve the appropriate amount of daylight in apartment houses in Tehran. City Identity, 25, 41-50.


3. Altomonte, S. (2008). Daylight for energy savings and psycho-physiological well-being in sustainable built environments. Journal of Sustainable Development, 1(3), 3-16.


4. Arntsen, T.-A., & Hrynyszyn, B. D. (2021). Optimization of Window Design for Daylight and Thermal Comfort in Cold Climate Conditions. Energies, 14(23), 8013.


5. Attia, S. (2012). Computational Optimisation for Zero Energy Building Design, Interviews with Twenty Eight International Experts. Retrieved from


6. Azadeh, A., Ghaderi, S., & Sohrabkhani, S. (2008). A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran. Energy policy, 36(7), 2637-2644.


7. Brzezicki, M. (2021). An Evaluation of Useful Daylight Illuminance in an Office Room with a Light Shelf and Translucent Ceiling at 51° N. Buildings, 11(11), 494.


8. Echenagucia, T. M., Capozzoli, A., Cascone, Y., & Sassone, M. (2015). The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis. Applied Energy, 154, 577-591.


9. Edwards, L., & Torcellini, P. (2002). Literature review of the effects of natural light on building occupants.


10. Fazeli, N., Mahdavinejad, M., & Bemanian, M. (2019). Dynamic Envelope and Control Shading Pattern for Office Buildings Visual Comfort in Tehran. Space Ontology International Journal, 8(3), 31-40.


11. Ferdyn-Grygierek, J., & Grygierek, K. (2017). Multi-variable optimization of building thermal design using genetic algorithms. Energies, 10(10), 1570.


12. Heydari, S. (2012). Architecture and Lighting. Tehran: University of Tehran.


13. Huizenga, C., Zhang, H., Mattelaer, P., Yu, T., Arens, E. A., & Lyons, P. (2006). Window performance for human thermal comfort.


14. Husin, S. N. F. S., & Harith, Z. Y. H. (2012). The performance of daylight through various type of fenestration in residential building. Procedia-Social and Behavioral Sciences, 36, 196-203.


15. Krarti, M., Erickson, P. M., & Hillman, T. C. (2005). A simplified method to estimate energy savings of artificial lighting use from daylighting. Building and environment, 40(6), 747-754.


16. Kwon, H. J., Yeon, S. H., Lee, K. H., & Lee, K. H. (2018). Evaluation of building energy saving through the development of venetian blinds’ optimal control algorithm according to the orientation and window-to-wall Ratio. International Journal of Thermophysics, 39(2), 1-27.


17. Laura Bellia, F. F., and Alessia Pedace. (2015). Evaluation of Daylight Availability for Energy Savings. Journal of Daylighting 2, 12-20.


18. Lechner, N. (2014). Heating, cooling, lighting: Sustainable design methods for architects: John wiley & sons.


19. Maleki, A., & Dehghan, N. (2021). Optimum Characteristics of Windows in an Office Building in Isfahan for Save Energy and Preserve Visual Comfort. Journal of Daylighting, 8(2), 222-238.


20. Mangkuto, R. A., Rohmah, M., & Asri, A. D. (2016). Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics. Applied Energy, 164, 211-219.


21. Mebarki, C., Djakab, E., Mokhtarii, A. M., & Amrane, Y. (2021). Improvement of Daylight Factor Model for Window Size Optimization and Energy Efficient Building Envelope Design. Journal of Daylighting, 8(2), 204-221.


22. Moulaii, M., Pilechiha, P., & Shadanfar, A. (2019). Optimization of window proportions with an approach to reducing energy consumption in office buildings. Naqshejahan-Basic studies and New Technologies of Architecture and Planning, 9(2), 117-123.


23. Muñoz, C. M., Esquivias, P. M., Moreno, D., Acosta, I., & Navarro, J. (2014). Climate-based daylighting analysis for the effects of location, orientation and obstruction. Lighting Research & Technology, 46(3), 268-280.


24. Nabil, A., & Mardaljevic, J. (2005). Useful daylight factors. Energy and Buildings, 38(7), 316-328.


25. Nguyen, A.-T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, 1043-1058.


26. Nikolaidou, E., Wright, J., & Hopfe, C. (2017). Robust building scheme design optimization for uncertain performance prediction.


27. Ochoa, C. E., Aries, M. B., van Loenen, E. J., & Hensen, J. L. (2012). Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort. Applied Energy, 95, 238-245.


28. Pilechiha, P., Bayat, M., & Ghasemi Nasab, M. (2021). Energy Optimization of Double Glazed Window Parameters in Hot and Arid Climate (Case S‌tudy: the Southern Front of an Office Building in Tehran). Hoviatshahr, 15(3), 5-14.


29. Pilechiha, P., Mahdavinejad, M., Rahimian, F. P., Carnemolla, P., & Seyedzadeh, S. (2020). Multi-objective optimisation framework for designing office windows: quality of view, daylight and energy efficiency. Applied Energy, 261, 114356.


30. Ruck, N., Aschehoug, Ø., & Aydinli, S. (2000). Daylight buildings. A source book on daylighting systems and components.


31. Shaeri, J., Habibi, A., Yaghoubi, M., & Chokhachian, A. (2019). The optimum window-to-wall ratio in office buildings for hot‒humid, hot‒dry, and cold climates in Iran. Environments, 6(4), 45.


32. Shahbazi, Y., Heydari, M., & Haghparast, F. (2019). An early-stage design optimization for office buildings’ façade providing high-energy performance and daylight. Indoor and Built Environment, 28(10), 1350-1367.


33. Su, X., & Zhang, X. (2010). Environmental performance optimization of window–wall ratio for different window type in hot summer and cold winter zone in China based on life cycle assessment. Energy and buildings, 42(2), 198-202.


34. Susorova, I., Tabibzadeh, M., Rahman, A., Clack, H. L., & Elnimeiri, M. (2013). The effect of geometry factors on fenestration energy performance and energy savings in office buildings. Energy and Buildings, 57, 6-13.


35. Torres, A., Mahmoudi, B., Darras, A., Imanpour, A., & Driver, R. (2021). Achieving an Optimized Solution for Structural Design of Single-Storey Steel Buildings using Generative Design Methodology (2516-2314). Retrieved from


36. Torres, S. L., & Sakamoto, Y. (2007). Facade design optimization for daylight with a simple genetic algorithm. Paper presented at the Proceedings of Building Simulation.


37. Tregenza, P., & Wilson, M. (2013). Daylighting: architecture and lighting design: Routledge.


38. Vanhoutteghem, L., Skarning, G. C. J., Hviid, C. A., & Svendsen, S. (2015). Impact of façade window design on energy, daylighting and thermal comfort in nearly zero-energy houses. Energy and Buildings, 102, 149-156.


39. Wallacie, W. P., ” vol. 2019, no. November, 2019, [Online]. Retrieved from https://www.wallacei.com/


40. WATTS, K. MULTI OBJECTIVE OPTIMIZATION OF FAÇADE GLAZING.


41. Wetter, M., & Wright, J. (2004). A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and environment, 39(8), 989-999.


42. Zeinalzadeh, T., Nikghadam, N., & Fayaz, R. (2021). Determining the Proportions of the Living Room to Optimize the Daylight Case Study: A Building with a Common Plan in Tehran. Space Ontology International Journal, 10(2), 1-17.


43. Zhai, Y., Wang, Y., Huang, Y., & Meng, X. (2019). A multi-objective optimization methodology for window design considering energy consumption, thermal environment and visual performance. Renewable energy, 134, 1190-1199.


44. Zhang, A., Bokel, R., van den Dobbelsteen, A., Sun, Y., Huang, Q., & Zhang, Q. (2017). Optimization of thermal and daylight performance of school buildings based on a multi-objective genetic algorithm in the cold climate of China. Energy and Buildings, 139, 371-384.


45. Zhao, J., & Du, Y. (2020). Multi-objective optimization design for windows and shading configuration considering energy consumption and thermal comfort: A case study for office building in different climatic regions of China. Solar Energy, 206, 997-1017.


46. Zomorodian, Z., Korsavi, S., & Tahsildoost, M. (2016). The effect of window configuration on daylight performance in classrooms: A field and simulation study. Iran University of Science & Technology, 26(1), 15-24.