Evaluating the Ability of eQUEST Software to Simulate Low-energy Buildings in a Cold Climatic Region
Bibliographic record
Abstract
Building Simulation is widely used for understanding how a building consumes energy and for assessing design strategies aimed at improving building energy efficiency. The present research study uses eQUEST, a popular simulation software. Various simulations are done here to analyse and critically comment on the best design strategies to be used in order to vastly reduce the energy consumption of a recently constructed small (1800 m2 floor area) commercial building in Brampton, Ontario, which is a heating dominated region. The limitations faced with eQUEST while simulating the modified design are critiqued. A complete understanding of the building science and heat flow through the building envelope has been applied to modify the building in question. After all the changes applied, the overall heat load of the building was reduced to 15 kWh/m2/yr and the overall energy consumption reduced by 60 percent.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".