| id |
caadria2024_273 |
| authors |
Li, Xiaoqian, Han, Zhen, Liu, Gang and Stouffs, Rudi |
| year |
2024 |
| title |
A Rapid Prediction Model for View-Based Glare Performance With Multimodal Generative Adversarial Networks |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 29–38 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.029
|
| summary |
Machine learning-based glare prediction has greatly improved the efficiency of performance feedback. However, its limited generalizability and the absence of intuitive predictive indicators have constrained its practical application. In response, this study proposes a prediction model for luminance distribution images based on the multimodal learning approach. This model focuses on objects within the field of view, integrating spatial and material features through images. It also employs semantic feature mapping and multimodal data integration to flexibly represent building information, removing limitations on model validity imposed by changes in design scenarios. Additionally, the study proposes a multimodal Generative Adversarial Network tailored for the multimodal inputs. This network is equipped with unique feature fusion and reinforcement blocks, along with advanced up-sampling techniques, to efficiently distill and extract pertinent information from the inputs. The model's efficacy is verified by cases focusing on residential building luminance distribution, with a 97% improvement in computational speed compared to simulation methods. Offering both speed and accuracy, this model provides designers with a rapid, flexible, and intuitive supporting approach for daylight performance optimization design, particularly beneficial in the early design stage. |
| keywords |
Glare Prediction, Prediction Model, Multimodal Model, Generative Adversarial Networks |
| series |
CAADRIA |
| email |
lixiaoqian_95@tju.edu.cn |
| full text |
 file.pdf (985,843 bytes) |
| references |
Content-type: text/plain
|
Al Horr, Y., Arif, M., Kaushik, A., Mazroei, A., Katafygiotou, M., & Elsarrag, E. (2016)
 Occupant productivity and office indoor environment quality: A review of the literature
, Building and environment, 105, 369-389. https://doi.org/10.1016/j.buildenv.2016.06.001
|
|
|
|
|
Ayoub, M. (2019)
A multivariate regression to predict daylighting and energy consumption of residential buildings within hybrid settlements in hot-desert climates
, Indoor and built environment, 28(6), 848-866. https://doi.org/10.1177/1420326X18798164
|
|
|
|
|
Giovannini, L., Favoino, F., Verso, V. R. M. L., Serra, V., & Pellegrino, A. (2020)
GLANCE (GLare ANnual Classes Evaluation): An approach for a simplified spatial glare evaluation
, Building and Environment, 186, 107375. https://doi.org/10.1016/j.buildenv.2020.107375
|
|
|
|
|
Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017)
Image-to-image translation with conditional adversarial networks
, Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134)
|
|
|
|
|
Jones, N. L., & Reinhart, C. F. (2019)
Effects of real-time simulation feedback on design for visual comfort
, Journal of Building Performance Simulation, 12(3), 343-361
|
|
|
|
|
Jones, N. L. (2019)
Fast climate-based glare analysis and spatial mapping
, Proceedings of building simulation 2019: 16th Conference of IBPSA
|
|
|
|
|
Lin, C. H., & Tsay, Y. S. (2021)
A metamodel based on intermediary features for daylight performance prediction of facade design
, Building and Environment, 206, 108371
|
|
|
|
|
Liu, Y., Colburn, A., & Inanici, M. (2020)
Deep neural network approach for annual luminance simulations
, Journal of Building Performance Simulation, 13(5), 532-554
|
|
|
|
|
Luo, Z., Sun, C., Dong, Q., & Qi, X. (2022)
Key control variables affecting interior visual comfort for automated louver control in open-plan office--a study using machine learning
, Building and Environment, 207, 108565 https://doi.org/10.1016/j.buildenv.2021.108565
|
|
|
|
|
Mostafavi, F., Tahsildoost, M., Zomorodian, Z. S., & Shahrestani, S. S. (2022)
An interactive assessment framework for residential space layouts using pix2pix predictive model at the early-stage building design
, Smart and Sustainable Built Environment
|
|
|
|
|
Ngarambe, J., Adilkhanova, I., Uwiragiye, B., & Yun, G. Y. (2022)
A review on the current usage of machine learning tools for daylighting design and control
, Building and Environment, 109507. https://doi.org/10.1016/j.buildenv.2022.109507
|
|
|
|
|
Nourkojouri, H., Shafavi, N. S., Tahsildoost, M., & Zomorodian, Z. S. (2021)
Development of a machine-learning framework for overall daylight and visual comfort assessment in early design stages
, Journal of Daylighting, 8(2), 270-283. https://doi.org/10.15627/jd.2021.21
|
|
|
|
|
Pierson, C., Wienold, J., & Bodart, M. (2018)
Review of factors influencing discomfort glare perception from daylight
, Leukos, 14(3), 111-148. https://doi.org/10.1080/15502724.2018.1428617
|
|
|
|
|
Radziszewski, K., & Waczyńska, M. (2018)
Machine learning algorithm-based tool and digital framework for substituting daylight simulations in early-stage architectural design evaluation
, Proceedings of the Symposium on Simulation for Architecture and Urban Design (pp. 1-7)
|
|
|
|
|
Wu, W., Fu, X. M., Tang, R., Wang, Y., Qi, Y. H., & Liu, L. (2019)
Data-driven interior plan generation for residential buildings
, ACM Transactions on Graphics (TOG), 38(6), 1-12. https://doi.org/10.1145/3355089.3356556
|
|
|
|
|
Xie, F., Song, H., & Zhang, H. (2023)
Research on Light Comfort of Waiting Hall of High-Speed Railway Station in Cold Region Based on Interpretable Machine Learning
, Buildings, 13(4), 1105. https://doi.org/10.3390/buildings13041105
|
|
|
|
|
Xie, J., & Sawyer, A. O. (2021)
Simulation-assisted data-driven method for glare control with automated shading systems in office buildings
, Building and Environment, 196, 107801. https://doi.org/10.1016/j.buildenv.2021.107801
|
|
|
|
| last changed |
2024/11/17 22:05 |
|
