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. ... Read more

Optimizing the built environment via simulations of building models hinges on standardizing data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analyzed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties showed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% to 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help to guide a more efficient data acquisition process of indoor spaces for daylight simulations. ... Read more

3D modeling of indoor spaces is a prerequisite for daylight simulation, and the accuracy of the 3D models has a significant impact on the simulation. The goal of this study was to quantify the errors caused by modeling indoor spaces at different accuracy levels to find the optimal balance between the reliability of the results and labor investment. For this purpose, we introduce a level of detail (LOD) concept for indoor spaces based on the size of non-permanent indoor objects by inclusion and exclusion from the simulation scene. The errors corresponding to models with low accuracies are measured by climate-based simulation using an improved two-phase method. Our results show that inaccurate modeling of indoor spaces causes between 10-70% error in TAI with 25% median across all spaces. ... Read more

Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known. ... Read more

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 ... Read more