Occupant monitoring, modelling, and simulation to improve office design and operation
Bibliographic record
Abstract
Building engineers and managers often treat occupants as passive agents despite their important role in building energy performance.Meanwhile, neglecting occupants' preferences may lead to poor implementations of building controls.The overall goal of this research is to improve buildings' energy performance and occupants' satisfaction by considering occupant-building interactions.This research first assessed the impact of static and dynamic occupant modelling approaches in a simulation-based analysis using the existing occupant models.The results showed the discrepancies between these two approaches in predicting the energy performance.The analysis emphasized the importance of using dynamic modelling approach.Given that the existing occupant models are context-specific, the next steps of this research focused on conducting a monitoring campaign.Therefore, the various monitoring methods exist in the literature and the anecdotal evidence were critically reviewed.Among the methods, environmental conditions are more controllable as well as configuring building designs and control systems are more flexible with laboratories and virtual environments.However, researchers can achieve valuable information on the natural occupants' presence and behaviour by in-situ monitoring on a relatively large sample size at a lower cost in long-term studies.This research adopted the in-situ method in a case study.A monitoring campaign was conducted in an office building in Ottawa, Canada.The monitoring study aimed to: (1) extract lessons beneficial for buildings' operation and design, and (2) improve the existing building's operation and occupants' satisfaction.Among the various domains covered in the exploratory analysis, the occupancy-based lighting control was found not to reduce the lighting energy use and satisfy occupants.Therefore, the control system was adjusted to the manual-on/vacancy-off.The results indicated a reduction in the lighting use by a factor of seven.The exploration of several fundamental occupant modelling issues revealed that start date and duration of a study are influential factors on the reliability of models.The findings of this research indicate the great potential of considering occupantbuilding interactions for the betterment of buildings' energy performance and occupants' satisfaction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".