Climate-based daylight modeling, metrics, and level of detail

Climate-Based Daylight Modelling (CBDM) differs from other daylight simulation techniques in that it can produce representative results in physical and photometric units. The typical application of CBDM during the design of buildings or the planning of interventions however implies incomplete information, e.g. on the definitive furnishing, selection of surface properties, and installation of shades and glare controls. This is reflected by uncertainties and reduced level of detail (LoD) in the setup of models. Relying on a detailed model of an existing residential unit in Singapore, we assess the impact LoD by sequentially reducing model detail. Impact is assessed in terms of a) absolute quantities, e.g. illuminance, b) thresholded metrics, e.g. Daylight Autonomy and Daylight Glare Probability, and c) relative ranking based on these metrics. We demonstrate the implementation of the efficient zonal daylight simulation with Raytraverse and Radiance, and the processing of the spatio-temporal results with Python (and its numpy and scipy modules). An outlook into the possible extension of the study by accessing a building database is provided.