A Multi-Objective Optimization Framework for Designing Kinetic Shading Patterns based on Daylight and Lighting Energy Efficiency
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".