| id |
cf2019_010 |
| authors |
Lorenz, Clara-Larissa; Bleil De Souza, Spaeth and Packianather |
| year |
2019 |
| title |
Machine Learning in Design Exploration: An Investigation of the Sensitivities of ANN-based Daylight Predictions |
| source |
Ji-Hyun Lee (Eds.) "Hello, Culture!" [18th International Conference, CAAD Futures 2019, Proceedings / ISBN 978-89-89453-05-5] Daejeon, Korea, pp. 75-87 |
| summary |
The use of Artificial Neural Networks (ANNs) promises greater efficiency in the assessment of daylight situations than simulations. With the daylight factor under scrutiny and the recent adaptation of climate-based daylight metrics in British and European buildings standards, ANNs provide a possibility for instantaneous feedback on otherwise time-consuming performance metrices. This study demonstrates the application of ANNs as prediction systems in design exploration. A specific focus of the research is the flexibility of ANNs, their reliability and sensitivity to changes. |
| keywords |
Artificial neural networks, atria, climate-based daylight modeling, daylight autonomy, daylight performance, parametric design |
| series |
CAAD Futures |
| email |
|
| full text |
 file.pdf (962,686 bytes) |
| references |
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| last changed |
2019/07/29 14:08 |
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