| id |
caadria2021_052 |
| authors |
Yousif, Shermeen and Bolojan, Daniel |
| year |
2021 |
| title |
Deep-Performance - Incorporating Deep Learning for Automating Building Performance Simulation in Generative Systems |
| doi |
https://doi.org/10.52842/conf.caadria.2021.1.151
|
| source |
A. Globa, J. van Ameijde, A. Fingrut, N. Kim, T.T.S. Lo (eds.), PROJECTIONS - Proceedings of the 26th CAADRIA Conference - Volume 1, The Chinese University of Hong Kong and Online, Hong Kong, 29 March - 1 April 2021, pp. 151-160 |
| summary |
In this study, we introduce a newly developed method called Deep-Performance, to enable automatic environmental performance simulation prediction without the need to perform simulations, by integrating deep learning strategies. The aim is to train neural networks on datasets with thousands of building design samples and their corresponding performance simulation. The trained model would offer performance prediction for design options emerging in generative protocols. The research is a work-in-progress within a broader project aimed at automating buildings environmental performance evaluations of daylight analysis and energy simulation, using deep learning (DL) models. This paper focuses on the implementation of a supervised DL method for automating the retrieval of daylight analysis metrics, targeting successful daylight design and higher building enclosure efficiency. We have further improved a Pix2Pix model trained on 5 different datasets, each containing 6000 paired images of architectural floor plans and their daylight simulation metrics. In the inference phase, the model was able to accurately predict the daylight simulation for unseen sets of floor plans. For validation, two quantitative assessment metrics were followed to assess the predicted daylight performance against the daylight performance simulation. Both assessment metrics showed high accuracy levels. |
| keywords |
Deep Learning; Artificial Intelligence; Deep-Performance; Automating Building Performance Simulation; Generative Systems |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (8,832,595 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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