Parametric model development for building input variables for lighting and shading controls for different building form factors

The combined use of dynamic shading and lighting controls can significantly reduce energy consumption while enhancing occupant’s visual comfort. Previous studies have used parametric and optimization methods to determine the ideal combination of building input variables for these shading and lighting controls. These studies often focus on maximizing energy savings and daylight availability using metrics such as Energy Use Intensity (EUI) and Useful Daylight Illuminance (UDI). However, relying solely on EUI and UDI overlooks factors like excessive glare, outdoor views, and thermal comfort, which may result in suboptimal solutions. This study aims to determine the optimal configurations of input variables, such as Window-to-Wall Ratio (WWR) for all four cardinal directions, shade properties, and window overhang depth for dynamically controlled roller shades for different building form factors with the objective is to optimize EUI, UDI, outdoor view, and thermal comfort. This study uses Honeybee and Ladybug to develop a daylighting model and an energy model using Rhino/Grasshopper as the interface for parametric modeling. The findings indicate that for two-variable combinations involving EUI (e.g. EUI and UDI, EUI and View, EUI and PMV), optimal shading and window-to-wall ratio (WWR) strategies depend on orientation. For South and West-facing façades, two patterns of solutions provide the best results, (i) low shade openness factor (1%) paired with high WWR values (50–75%) and (ii) high shade openness factor (5–10%) combined with low WWR values (25–50%) both perform equally well. No clear pattern for WWR emerges for North-facing façades. Regarding shade overhang depth, square buildings perform slightly better without overhangs, while rectangular buildings benefit more from a 1-meter overhang, though the difference is minimal. Overall, the paper presents a framework for analyzing the output of a multi-input daylight and energy simulation model, where multiple output metrics must be optimized simultaneously. By leveraging the Pareto front, the study identifies the best possible combinations of input variables to achieve optimal performance.