Modelling Daylight for Existing Indoor Spaces: Towards formalisation and automation of input data for simulations

Towards formalisation and automation of input data for simulations

Abstract

Despite the maturity of physically based daylight simulation tools, their broad applicability to existing buildings remains constrained. This is partly due to the lack of formal definitions that ensure comparability among models created in different contexts, partly due to inefficient techniques for input acquisition, and partly due to gaps in model calibration. This work addresses these limitations by first defining different levels of geometric agreement between digital and real indoor spaces, termed Geometrical Levels of Detail (GLoD). These levels represent degrees of geometric completeness and resolution.

The study quantifies how those degrees of representation translate into errors in daylight simulation results. A similar framework is introduced for material inputs through Material Classes of Precision (MCoP). These classes represent different techniques for acquiring optical properties. The propagated uncertainty associated with each level of precision is systematically analysed to determine its influence on daylight simulation results. Third, a semi-automatic pipeline is developed to reconstruct simulation-ready geometry from LiDAR point clouds. The workflow includes preprocessing, watertight reconstruction of permanent objects, and detection and reconstruction of window boundaries with minimal user interaction. Its performance is evaluated using daylight availability and glare metrics. Fourth, image-based material characterisation techniques are assessed as accessible alternatives to laboratory measurements.

Three techniques are validated, and their influence on daylight simulation results is quantified. A spectral uplifting method is further evaluated to reconstruct full spectral reflectance from RGB inputs for spectral daylight simulations. Finally, a calibration workflow for indoor spectral daylight simulation is introduced to account for uncertainties related to exterior conditions and window characterisation. Measured spectral irradiance data are used to minimise simulation error. Together, these contributions enable practitioners and researchers to create a robust digital daylight model for existing indoor spaces.