Content
Number of images - 4
Tables and charts - 4
Efficient Calculation of Annual Daylighting Performance Metrics Using the Klems Grid L&E, Vol.32, No.4, 2024

Light & Engineering 32 (4) 2024

Volume 32
Date of publication 08/15/2024
Pages 22–31

Purchase PDF - ₽600

Efficient Calculation of Annual Daylighting Performance Metrics Using the Klems Grid L&E, Vol.32, No.4, 2024
Articles authors:
Alexey G. Voloboy, Sergey V. Ershov, Eugene Yu. Denisov, Vladimir A. Galaktionov

Alexey G. Voloboy, Doctor of Physical and Mathematical Sciences. In 1988, he graduated from the Faculty of Mechanics and Mathematics of Moscow State University named after M.V. Lomonosov. He defended his Ph. D. thesis at the Keldysh Institute of Applied Mathematics RAS in 2005, doctoral dissertation was defended in 2012. At present, he is the leading researcher at the Department of Computer Graphics and Computational Optics at the KIAM RAS. Area of his scientific interests: computer graphics, computational optics, ray tracing, lighting simulation. He is the author of more than 200 articles

Sergey V. Ershov, Ph. D. of Phys. – Math. Sciences. He graduated from the Faculty of Physics of Moscow State University named after M.V. Lomonosov in 1988. He defended his Ph. D. thesis at the Keldysh Institute of Applied Mathematics RAS in 1991. Currently, he is the senior researcher in Department of Computer Graphics and Computational Optics at the KIAM RAS. Area of his scientific interests: computer graphics, computational optics, numerical methods of math. physics, Monte Carlo methods, ray tracing, diffraction problems. He is the author of more than 100 articles on this subject

Eugene Yu. Denisov, He graduated from the Faculty of Computational Mathematics and Cybernetics of Moscow State University named after M.V. Lomonosov in 1995. Currently, he is the researcher in Department of Computer Graphics and Computational Optics at the KIAM RAS. Area of his scientific interests: computer graphics, ray tracing, lighting simulation, automation of program testing. He is the author of 24 articles on this topic

Vladimir A. Galaktionov, Doctor of Physical and Mathematical Sciences, Professor. He graduated from the Faculty of Management and Applied Mathematics of the Moscow Institute of Physics and Technology in 1975. He defended his Ph. D. thesis at the Keldysh Institute of Applied Mathematics RAS in 1982 and doctoral dissertation in 2006. At present, he is the chief researcher of the Department of Computer Graphics and Computational Optics at the KIAM RAS. Areas of his scientific interest: computer graphics, computational optics, computational linguistics, scientific visualization. He is the author of more than 250 articles

Abstract:
Natural lighting (daylight) plays a decisive role in energy saving and building maintenance. An important aspect is the comfort of the premises. Modern building design requires computer analysis of indoor illumination in accordance with the green building certification program, the main indicators of which are spatial daylight autonomy (sDA) and annual sunlight exposure (ASE). Calculation of these metrics requires thousands of simulations of global illumination for various time moments throughout the year. Well-known methods of lighting simulation are not effective here. We propose an original approach that uses precomputed data and its fast interpolation on a Klems grid to iteratively simulate the lighting distribution. The method was compared with classical stochastic ray tracing and existing solutions based on the radiosity method. The efficiency of calculations has increased significantly (and sometimes orders of magnitude) while maintaining high accuracy of the results. The ability to calculate metrics in tens of minutes on a regular computer, provided by our method, allows an architectural design to be continuously checked for compliance with the green buildings program.
References:
1. U.S. Green Building Council (USGBC). LEED v4.1 Building Design + Construction Guide, EQ Credit: Daylight. URL: https://www.usgbc.org/leed/v41 (accessed 25.03.2024).
2. Ayoub, M. 100 Years of daylighting: A chronological review of daylight prediction and calculation methods // Solar Energy, 2019, Vol. 194, pp. 360–390. doi: 10.1016/j.solener.2019.10.072
3. DesignBuilder Daylighting. URL: https://designbuilder.co.uk/daylighting (accessed 25.03.2024).
4. Climate Studio. URL: https://www.solemma.com/climatestudio (accessed 25.03.2024).
5. Daylight Autonomy. URL: https://deluminaelab.com/dl-light/en/autonomy.html (accessed 25.03.2024).
6. Ward, G.J. The RADIANCE lighting simulation and rendering system // Proceedings of the 21st annual conference on Computer graphics and interactive techniques SIGGRAPH’94, July 1994, pp. 459–472. doi: 10.1145/192161.192286
7. Reinhart, C., Walkenhorst, O. Dynamic RADIANCE-Based Daylight Simulations for a Full-Scale Test Office with Outer Venetian Blinds // Energy and Buildings, 2001, Vol. 33, # 7, pp. 683–697.
8. Xinxin, L., Jin, H., Kang, J., Wu, H. A Simplified Method of Calculating Daylight Autonomy Through Spatial Parameters for Atriums in Shopping Streets // Proceedings of the 16th IBPSA Conference, 2019, pp. 417–424. doi: 10.26868/25222708.2019.210910
9. Lin, C.-H., Tsay, Y-S. A metamodel based on intermediary features for daylight performance predictionof facade design // Buildings and Environment, 2021, Vol. 206, 108371. doi: 10.1016/j.buildenv.2021.108371
10. 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 // Buildings and Environment, 2021, Vol. 200, 107932. doi: 10.1016/j.buildenv.2021.107932
11. Liu, Y., Colburn, A., Inanici, M. Deep neural network approach for annual luminance simulations // Journal of Buildings Performance Simulation, 2020, Vol. 13, # 5, pp. 532–554. doi: 10.1080/19401493.2020.1803404.
12. Lorenz, C.L., Jabi, W. Predicting daylight autonomy metrics using machine learning // Proceedings of the International Conference for Sustainable Design of the Built Environment (SDBE), 2017, pp. 991–1002.
13. Kazanasmaz, T., Günaydin, M., Binol, S. Artificial neural networks to predict daylight illuminance in office buildings // Buildings and Environment, 2009, Vol. 44, # 8, pp. 1751–1757. doi: 10.1016/j.buildenv.2008.11.012
14. Ngarambe, J., Irakoze, A., Yun, G.Y., Kim, G. Comparative performance of machine learning algorithms in the prediction of indoor daylight illuminances // Sustainability, 2020, Vol. 12, # 11, 4471. doi: 10.3390/su12114471
15. Ayoub, M. A review on machine learning algorithms to predict daylighting inside buildings // Solar Energy, 2020, Vol. 202, pp. 249–275. doi: 10.1016/j.solener.2020.03.104
16. Ershov, S.., Sokolov, V., Voloboy, A., Galaktionov, V. Effective Simulation of Spatial Daylight Autonomy and Annual Sunlight Exposure // Proceedings of the 32 International Conference on Computer Graphics and Vision Graphicon2022, 2022, pp. 64–72. doi: 10.20948/graphicon-2022-64-72.
17. Illuminating Engineering Society of North America (2013). IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE). (IES LM‑83–12). URL: https://store.ies.org/product/ies-spatial-daylightautonomy-sda-and-annual-sunlight-exposure-ase (accessed 08.08.2018).
18. Van Den Wymelenberg, K., Mahic, A. Annual Daylighting Performance Metrics, Explained // The journal of the American institute of architects, 2016. URL: https://www.architectmagazine.com/technology/lighting/annual-daylightingperformance-metrics-explained_o (accessed 25.03.2024).
19. Ruiz, A., Campano, M.A., Acosta, I., Luque, O. Partial Daylight Autonomy (DAp): A New Lighting Dynamic Metric to Optimize the Design of Windows for Seasonal Use Spaces // Applied Sciences, 2021, Vol. 11, 8228. doi: 10.3390/app11178228
20. Park, K.-W., Athienitis, A.K. Workplane illuminance prediction method for daylighting control systems // Solar Energy, 2003, Vol. 75, # 4, pp. 277–284. doi: 10.1016/j.solener.2003.08.013
21. Ershov, S., Sokolov, V., Galaktionov, V., Voloboy, A. Virtual Light Sensing Technology for Fast Calculation of Daylight Autonomy Metrics // Sensors, 2023, Vol. 23, # 4, 2255. doi: 10.3390/s2304225
22. Perez, R., Seals, R., Ineichen, P., Stewart, R., Menicucci, D. A new simplified version of the Perez diffuse irradiance model for tilted surfaces // Solar Energy, 1987, Vol. 39, # 3, pp. 221–232.
23. Perez, R., Ineichen, P., Seals, R., Michalsky, J., Stewart, R. Modelling daylight availability and irradiance components from direct and global irradiance // Solar Energy, 1990, Vol. 44, # 5, pp. 271–289.
24. Meeus, J. Astronomical Algorithms. – 2nd ed., Willmann-Bell, USA, 1998.
25. Probst O. The apparent motion of the Sun revisited // Eur. J. Phys. 2002, Vol. 23, pp. 315–322.
26. Climate. OneBuilding.Org. URL: https://climate.onebuilding.org (accessed 25.03.2024)
27. Pharr M., Jakob W., Humphreys G. Physically Based Rendering: from Theory to Implementation. – 3rd ed., Morgan Kaufmann, USA, 2017.
28. Rogers, Z., Thanachareonkit, A., Fernandes, L. Enhanced Skylight Modeling and Validation, Final report, URL: https://newbuildings.org/wp-content/uploads/2015/11/SkylightModelingValidation1.pdf (accessed 21.03.2024).
29. Merghani, A.H., Bahloul, S.A. Comparison between Radiance Daylight Simulation Software Results and Measured on-Site Data // Journal of Building and Road Research, 2016, Vol. 20, pp. 49–69. doi: 10.53332/jbrr.v20i.588
Keywords

Buy

Recommended articles