The impact of facade geometry on visual comfort and energy consumption in an office building in different climates
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".