Utilizing Pareto Optimality to Identify Multiobjective Optimized Multi-Unit Residential Building Unit Design with Respect to Daylighting and Energy Consumption
Bibliographic record
Abstract
Canadians spend most of their time in their dwelling. Meanwhile, the densification of cities has trended toward multi-unit residential buildings (MURB) becoming the dominant residential building type, yet their study has been neglected despite their prevalence throughout Canada. This building typology is known to provide poor visual comfort and can also be typically characterized by poor energy performance. These issues of inadequate daylighting and poor energy performance form a complex relationship and are often juxtaposed in regard to passive building design – design that benefits one of these aspects likely will hamper the other. Despite this, they have rarely been studied together, particularly in the MURB setting. This study investigated the complex relationship between these two variables within parameterized MURB units utilizing the novel climate-based daylight modelling (CBDM) and energy modelling tool, ClimateStudio. The results were analyzed through the lens of a Pareto optimality analysis, a multiobjective optimization method that generates a series of optimized solutions. Due to the lack of consensus in the daylighting field, other daylighting variables were investigated, including daylight schedules, various dynamic daylighting metrics, and proximity to a neighbouring building. The main results revealed which model iterations were deemed optimal based on the Pareto front generated in the analysis. These multiobjective optimized units and the trends between them were discussed to determine what can be utilized to guide future MURB design, such as maximizing north and south facades while minimizing east and west facades, designing balanced aspect ratios, and more.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".