Early stage Design Workflow for high Energy Performance Multi-storey Residential Buildings
Bibliographic record
Abstract
This paper presents a methodology to optimize building envelope energy performance for multi-storey residential buildings using a design performance model approach. Five analysis techniques, applied to a database of parametric simulation results, are proposed to derive information on various building performance features that can support early design decisions. Information may include optimal combination of design parameter values to achieve lowest energy consumption, or the relative impact of design parameters on a given design, such as a base case. A workflow template is established to provide support for the design process of energy efficient multi-storey residential buildings. This template can form a basis for the development of an interactive tool that integrates energy performance principles into early stage design decisions. The application of this methodology to a building in Vancouver (BC, Canada, 49°N) is presented as a case study. Results of this application demonstrates that adopting a specific combination of building envelope parameters, thermal load can be reduced by up to 85% as compared to a base case designed according to commonly built apartment buildings in the studied location.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".