Image-based Material Characterization for Daylight Simulation Using Illuminance-proxy and Artificial Neural Networks

Conference Paper (2022)
Author(s)

N. Forouzandeh (TU Delft - Building Physics)

Eleonora Brembilla (TU Delft - Building Physics)

John Alstan Jakubiec (University of Toronto)

Research Group
Building Physics
Copyright
© 2022 N. Forouzandeh Shahraki, E. Brembilla, John Alstan Jakubiec
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 N. Forouzandeh Shahraki, E. Brembilla, John Alstan Jakubiec
Research Group
Building Physics
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Pages (from-to)
171-178
ISBN (electronic)
978-80-11-02269-3
Reuse Rights

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Abstract

A key aspect of daylight modeling is the definition of material optical properties. Characterization of such properties in existing indoor spaces with current methods is a labour-intensive and time-consuming task, especially in surfaces with considerable visual complexity. Faster and more accurate estimations of such properties will lead to more efficient workflows. Towards this direction, the present work studied the feasibility of using two novel approaches i.e. illuminance-proxy and probabilistic image based material characterization methods for implementation in daylight modeling. These approaches are compared with two common techniques, namely the manual selection from a measured dataset and the use of illuminance/luminance measurements. According to the results, both novel techniques are able to predict spatiallyaveraged Daylight Autonomy, continuous Daylight Autonomy, and Useful Daylight Illuminance in 300-3000 lx range with less than 5% error

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