Energy Performance Comparison Of A High Density Mixed Use Building To Traditional Building Types
Bibliographic record
Abstract
This paper applies an urban simulation tool to explore the impact of density on operational and embodied energy and carbon using parametrically-generated models. The models were created using Grasshopper and Rhinoceros 5, and the simulations were performed by the Urban Modelling Interface (UMI). A high-density mixed-use building housing 10,000 residents is compared to base cases of traditional building use types housing the same population. The retail and office space of the mixed-use building s also compared to typical local retail and office building types. Building shape, insulation levels and structural materials were also varied to analyse their affect. Results showed that of the base cases, highly insulated low-rise apartments had the best performance at 67% and 50% reductions over to-code insulated single detached homes. Of the large mixed-use building cases, they all had similar energy reductions to low rise apartments but due to utilizing concrete, their embodied energy and carbon were much higher. The timber framed versions of the mixed-use cases achieved better energy performance and cut their embodied energy and carbon by over 70%. Important results were that as buildings become much more energy efficient, the proportion of energy and emissions embodied in the materials becomes significant. Overall building form, as well as the construction material must be considered to minimize energy use and emissions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".