Towards a universal ranking system for design parameters’ impact on buildings’ lifecycle energy
Bibliographic record
Abstract
Abstract The energy consumption of buildings depends on numerous factors that can be categorized in four major categories: geometry parameters; location; attributes of electric and mechanical systems; and behaviour of users. Most of the existing publications on ‘energy-consumption influencing parameters’ test the sensitivity of energy consumption to these inputs in a single building; not making it possible to correlate different projects. The purpose of this study is to evaluate the impact caused by the model when evaluating parameters. In this paper we have studied a series of nine real-world design projects in cold climate (Québec, Canada) to analyse the behaviour of thirteen design parameters. Among the four major categories mentioned above, our scope is limited to geometry parameters (variation in climate, mechanical systems and occupants is excluded). The parameters include building orientation; window-to-wall ratio; overhang size; insulation; and Solar Heat Gain Coefficient (SHGC) for windows. All parameters are analysed using the Morris method for sensitivity analysis and are ranked based on the simulation results. According to the results, window-to-wall ratio and orientation show lower variation among different models while insulation, overhangs and SHGC appear to be more sensitive. The developed analysis is the starting point to what can be a shortcut for designers to control energy consumptions efficiently in new designs.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".