Assessment of the Impact of Window Size, Position and Orientation on Building Energy Load Using BIM
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 improving energy efficiency of buildings, windows play a significant role as they largely influence the energy load. Although there are many studies about the energy efficient window design, a rigorous study is missing which analyzes the mutual impact of windows’ size, position and orientation on the energy load. This study aims to address this gap through a case study on a single family house. For this aim, 65 different design scenarios are created which vary by window size, position and orientation. Building information models (BIMs) are created for each scenario via Autodesk Revit®, and are used for the calculation of the total energy load conducted by Autodesk Green Building Studio®. In the first analysis stage, window-to-wall ratio (WWR) and the windows’ position are studied to assess their effect on the energy load. The preliminary results at this stage indicate that the total energy load increases when the WWR grows, and the windows’ position has the biggest impact on the load when the WWR is 20. Using these results, in the next stage, the position of windows in different orientation is studied to assess how the energy load changes by windows’ position in each orientation. The results show that the building requires the lowest load when the windows are located in the middle height in all orientations, and the east windows’ positioning affects the total energy load the most.
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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