A Multi-Objective Optimization Framework for Designing Kinetic Shading Patterns based on Daylight and Lighting Energy Efficiency

Abstract

Escalating environmental issues—such as climate change, rising energy use, and poor indoor environmental quality—have made performance-driven design essential. Designers often use extensive glazing to increase daylight, but this can cause uneven distribution, overheating, glare, and visual discomfort. Kinetic shading devices offer a solution, yet their use remains limited due to the high computational demands in the design process and the complexity of multi-objective optimization. This study investigates how parametric and kinetic shading systems can improve daylight performance and reduce lighting energy use in educational buildings, following LEED v4 Platinum standards. As a proof of concept, the study compares four globally recognized shading patterns, derived from real-world building applications, to identify the most effective strategy for optimizing daylight performance. Using parametric modeling, daylight simulations, and genetic algorithms, two key metrics—Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE)—are optimized. Various shading strategies are evaluated to enhance visual comfort, daylight uniformity, and indoor environmental quality. Results show that kinetic systems perform well across these metrics, supporting healthier, more energy-efficient spaces. This work lays the groundwork for integrating broader metrics such as daylight efficiency and renewable energy potential into sustainable design.