The impact of facade geometry on visual comfort and energy consumption in an office building in different climates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, there has been a heightened emphasis improving visual comfort and energy efficiency. Various solutions have been explored to achieve high-performance design. Shading devices play a crucial role in enhancing building performance by redusing solar gains, excessive daylight, and improving both energy efficiency and occupants' visual comfort. This research aims to investigate the effect of facade geometry on visual comfort and energy consumption in four different climates of Iran and categorize each variable based on effectiveness for each location. Parametric office modeling was done by using Grasshopper and Rhino software. Then, the effect of the facade on the interior lighting and energy consumption was analyzed by Radiance, Daysim, and EnergyPlus calculation engines. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) was selected to optimize solutions, minimize energy consumption, maximize useful daylight illuminance, and view quality. In addition, the methodology was used to explore the framework for optimizing office facade design in Iran's diverse climatic zones. The simulation results indicate that window-to-wall ratio and inclined wall were essential for balancing daylighting performance and energy consumption. This research stated that using a self-shading design could increase the quality of view up to 75% while reducing energy consumption and the risk of glare. Results proposed a design framework to improve visual comfort and save energy. The rotating façade's wall 10°-30° reduced cooling energy demand and energy usage intensity in selected models. So, an inclined wall could be an efficient shading device to improve building's performance in Iran.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it