Daylighting assessment of window layouts and architectural elements in early design stages
Son, P. V. H. & Huyen, V. T. B. Optimizing daylight in west-facing facades for LEED V4.1 compliance using metaheuristic approach. Sci. Rep. 13, 21942. (2023).
Google Scholar
Lou, S. et al. Multi-objective optimization of daylighting performance and solar radiation for Building geometry using a hybrid evolutionary algorithm. Sci. Rep. 15, 26644. (2025).
Google Scholar
Lee, J. & Boubekri, M. Impact of daylight exposure on health, well-being and sleep of office workers based on actigraphy, surveys, and computer simulation. J. Green. Building. 15, 19–42. (2020).
Google Scholar
Dogrusoy, I. T. & Tureyen, M. A field study on determination of preferences for windows in office environments. Build. Environ. 42, 3660–3668. (2007).
Google Scholar
Reinhart, C. F. & LoVerso, V. R. M. A rules of thumb-based design sequence for diffuse daylight. Lighting Res. Technol. 42, 7–31. (2010).
Google Scholar
Vanhoutteghem, L., Skarning, G. C. J., Hviid, C. A. & Svendsen, S. Impact of façade window design on energy, daylighting and thermal comfort in nearly zero-energy houses. Energy Build. 102, 149–156. (2015).
Google Scholar
Cammarano, S., Pellegrino, A., Lo Verso, V. R. M. & Aghemo, C. Assessment of daylight in rooms with different architectural features. Building Res. Inform. 43, 222–237. (2015).
Google Scholar
Kose, B. & Kazanasmaz, T. Applicability of a prismatic panel to optimize window size and depth of a south-facing room for a better daylight performance. Light Eng. 28, 63–67. (2020).
Google Scholar
Dubois, M. C. & Flodberg, K. Daylight utilisation in perimeter office rooms at high latitudes: investigation by computer simulation. Lighting Res. Technol. 45, 52–75. (2012).
Google Scholar
Acosta, I., Campano, M. Á. & Molina, J. F. Window design in architecture: analysis of energy savings for lighting and visual comfort in residential spaces. Appl. Energy. 168, 493–506. (2016).
Google Scholar
Berardi, U. & Anaraki, H. K. The benefits of light shelves over the daylight illuminance in office buildings in Toronto. Indoor Built Environ. 27, 244–262. (2016).
Google Scholar
Do, C. T. & Chan, Y-C. Daylighting performance analysis of a facade combining daylight-redirecting window film and automated roller shade. Build. Environ. 191, 107596. (2021).
Google Scholar
Wang, X., Teigland, R. & Hollberg, A. Identifying influential architectural design variables for early-stage Building sustainability optimization. Build. Environ. 252, 111295. (2024).
Google Scholar
Shen, H. & Tzempelikos, A. A parametric analysis for the impact of facade design options on the daylighting performance of office spaces. Internal High Performance Buildings Conference (2010).
Lee, J. W., Jung, H. J., Park, J. Y., Lee, J. B. & Yoon, Y. Optimization of Building window system in Asian regions by analyzing solar heat gain and daylighting elements. Renew. Energy. 50, 522–531. (2013).
Google Scholar
Goia, F. Search for the optimal window-to-wall ratio in office buildings in different European climates and the implications on total energy saving potential. Sol. Energy. 132, 467–492. (2016).
Google Scholar
Rubeis Td, Nardi, I., Muttillo, M., Ranieri, S. & Ambrosini, D. Room and window geometry influence for daylight harvesting maximization – Effects on energy savings in an academic classroom. Energy Procedia. 148, 1090–1097. (2018).
Google Scholar
Do, C. T. & Chan, Y-C. Evaluation of the effectiveness of a multi-sectional facade with Venetian blinds and roller shades with automated shading control strategies. Sol. Energy. 212, 241–257. (2020).
Google Scholar
You, W., Qin, M. & Ding, W. Improving Building facade design using integrated simulation of daylighting, thermal performance and natural ventilation. Build. Simul. 6, 269–282. (2013).
Google Scholar
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644. (2007).
Google Scholar
Kharvari, F. A. & Field-validated Multi-objective optimization of the shape and size of windows based on daylighting metrics in Hot-summer mediterranean and dry summer continental climates. J. Daylighting. 7, 222–237. (2020).
Google Scholar
Escobar, I., Orduna-Hospital, E., Aporta, J. & Sanchez-Cano, A. Efficient daylighting: the importance of glazing transmittance and room surface reflectance. Buildings 14 (2024).
Volf, C., Petersen, P. M., Thorseth, A., Vestergaard, S. & Martiny, K. Daylight quality: high-transmittance glass versus low transmittance glass – effects on daylight quality, health, comfort and energy consumption. Ann. Med. 56, 2297273. (2024).
Google Scholar
Wienold, J., Jain, S. & Andersen, M. Transmittance thresholds of electrochromic glazing to achieve annual low-glare work environments. E3S Web Conf. 362. (2022).
Illuminating Engineering Society. IES Spatial daylight autonomy (sDA) and annual sunlight exposure (ASE) (Standard LM-83-12). Illuminating Eng. Soc. North. America (2012).
Khanh Phuong, N. T., Chan, Y-C., Do, C. T., Tuan, N. A. & Rinchumphu, D. A simulation-based workflow to calculate overall thermal transfer value when implementing daylighting-oriented shading control. J. Building Eng. 84, 108616. (2024).
Google Scholar
USGBC. LEED v4.1 – Daylight, (accessed 1 August 2025); https://www.usgbc.org/credits/new-construction-schools-new-construction-retail-new-construction-data-centers-new-9
Alwetaishi, M. & Taki, A. Investigation into energy performance of a school Building in a hot climate: optimum of window-to-wall ratio. Indoor Built Environ. 29, 24–39. (2019).
Google Scholar
Environmental Design Solutions Ltd & Tas, E. D. S. L. (accessed 1 August 2025); https://www.edsl.net/
Schwartz, Y. & Raslan, R. Variations in results of Building energy simulation tools, and their impact on BREEAM and LEED ratings: A case study. Energy Build. 62, 350–359. (2013).
Google Scholar
Chi Fa, Wang, Y., Wang, R., Li, G. & Peng, C. An investigation of optimal window-to-wall ratio based on changes in Building orientations for traditional dwellings. Sol. Energy. 195, 64–81. (2020).
Google Scholar
Autodesk. Ecotect Analysis Discontinuation, F. A. Q. (accessed 23 July 2024); https://www.autodesk.com/support/technical/article/caas/sfdcarticles/sfdcarticles/Ecotect-Analysis-Discontinuation-FAQ.html
Ibarra, D. I. & Reinhart, C. F. Daylight factor simulations – how close do simulation beginners ‘really’ get? Building simulation 2009 11, 196–203 (2009).
Vangimalla, P. R., Olbina, S. J., Issa, R. R. & Hinze, J. Validation of Autodesk Ecotect™ accuracy for thermal and daylighting simulations. Proceedings of the Winter Simulation Conference (WSC) 3383–3394 (2011).
Crawley, D. B. et al. EnergyPlus: creating a new-generation Building energy simulation program. Energy Build. 33, 319–331. (2001).
Google Scholar
Ramos, G. & Ghisi, E. Analysis of daylight calculated using the energyplus programme. Renew. Sustain. Energy Rev. 14, 1948–1958. (2010).
Google Scholar
Yun, G. & Kim, K. S. An empirical validation of lighting energy consumption using the integrated simulation method. Energy Build. 57, 144–154. (2013).
Google Scholar
Reinhart, C. F. Tutorial on the use of daysim simulations for sustainable design. Ottawa: Institute for Research in Construction, National Research Council Canada (2006).
Reinhart, C. F. & Walkenhorst, O. Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds. Energy Build. 33, 683–697. (2001).
Google Scholar
Acosta, I., Muñoz, C., Esquivias, P., Moreno, D. & Navarro, J. Analysis of the accuracy of the Sky component calculation in daylighting simulation programs. Sol. Energy 119, 54–67. (2015).
Google Scholar
Bourgeois, D., Reinhart, C. F. & Ward, G. Standard daylight coefficient model for dynamic daylighting simulations. Building Res. Inform. 36, 68–82. (2008).
Google Scholar
Subramaniam, S. & Mistrick, R. A more accurate approach for calculating illuminance with daylight coefficients. 2017 Annual IES Conference Oregon, USA IES (2017).
Ladybug Tools & Tools, L. (accessed 1 August 2025); https://www.ladybug.tools/
Alsharif, R., Arashpour, M., Golafshani, E., Bazli, M. & Mohandes, S. R. Ensemble machine learning framework for daylight modelling of various Building layouts. Build. Simul. 16, 2049–2061. (2023).
Google Scholar
Hu, X., Zheng, H. & Lai, D. Prediction and optimization of daylight performance of AI-generated residential floor plans. Build. Environ. 279, 113054. (2025).
Google Scholar
Wang, D., Place, W., Li, S., Liu, R. & Hu, J. A simple yet powerful dimensionality reduction method for annual daylighting prediction and its inverse process via pix2pix. J. Building Eng. 105, 112410. (2025).
Google Scholar
Han, Y., Shen, L. & Sun, C. Developing a parametric morphable annual daylight prediction model with improved generalization capability for the early stages of office Building design. Build. Environ. 200, 107932. (2021).
Google Scholar
Ladybug, E. P. W. (accessed 1 August 1 2025); https://www.ladybug.tools/epwmap/
Robert, McNeel & Associates (accessed 1 August 2025);
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