CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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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 lorenzc4@cardiff.ac.uk
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