Sunlight-Daylight Signature: a Novel Concept to Assess Sunlight and Daylight Availability at Urban Scale

Daylighting and solar availability at urban scale has come to play a crucial role in the perception of discomfort conditions for people, both in outdoor and indoor spaces, and on the energy consumption of buildings. Daylighting and solar analyses are typically done separately. The paper presents a novel method, called the ‘sunlight-daylight signature’ (SDS), which allows the qualitative analysis of urban settings with respect to sunlight and daylight. The method can be used to classify different urban settings in terms of daylight/sunlight access or to test new development proposals by referring to existing locations and confirm whether a certain daylight quality is met. The SDS relies on a new analysis tool, called ‘sunlight-daylight wedge’ (SDW), which combines obstruction (through the vertical sky component VSC) and sunlight access (through the annual probable sunlight hours PASH and the winter probable sunlight hours PWSH). The orientation of the façade at each point is also included as it will affect the times of the day when the sun-hours from PASH and PWSH occur, thus affecting the character of the corresponding sunlight. The SDS approach is based on a clustering technique to subdivide large datasets (in this case, daylight data points across entire cities or major urban areas) into smaller groups, using machine learning by way of the k-medoids clustering technique. This is used to derive typical daylight and sunlight scenarios representing groups of data points with similar conditions. Additional data is included to account for urban density and daylight availability in public areas. Final output of the clustering process consists of a map showing areas with the same daylight signature (SDS), which means areas with the same sunlight and daylight conditions. The SDS can be useful for urban planners and building practitioners to predict the access to both daylight and sunlight of large urban settings to optimize comfort for people and energy usage.