Bibliographic record
Abstract
Occupant-related uncertainty has been recognized as one of the main challenges that building designers face. Current occupant modelling practices are based on simple assumptions that are typically made based on codes, standards, and rules-of-thumb. Designers assume occupants have homogeneous temporal and spatial distribution. This approach does not recognize differences among tenants or buildings and can lead to suboptimal design solutions that can compromise energy and comfort performance. Therefore, this doctoral research aims at developing a practical improved method for occupant modelling that recognizes occupant-related uncertainty. The method was developed based on a thorough qualitative and quantitative investigation. To this end, a deeper understanding of the current approaches, challenges and needs of occupant modelling throughout the design process was obtained and documented through a stakeholders' workshop and interviews with a case study design stakeholders. Then, a simulation-based investigation was conducted on a real case study office building located in Toronto, Canada. The simulation-based investigation included conducting a parametric analysis under variable occupant scenarios, developing an occupant-centric design optimization method, and evaluating the impact of occupants' spatial distribution on energy and comfort performance. The documentation of current occupant modelling practice indicated the need to improve the current approach by carefully considering occupant-related assumptions in early design stages. In addition, it indicated the need to improve communicating occupant-related assumptions among design stakeholders. The results of the simulation-based investigation indicated that occupant-related assumptions can influence the outcomes of design parametric analysis and design optimization. Notably, assumptions about occupants' spatial distributions demonstrated substantial impact on occupants' thermal comfort and the indoor air quality.
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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 | 0.018 | 0.019 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".