Evaluating the performance robustness of fixed and movable shading devices against diverse occupant behaviors
Bibliographic record
Abstract
Given the diverse operating conditions, weather conditions, space users, and occupant preferences of buildings, it is commonplace to provide occupants with multiple means to adapt their immediate indoor environment. However, numerous studies have shown that occupants sub-optimally use such controls to improve comfort during times of significant discomfort, but are much more passive when the source of discomfort is alleviated. Occupant-related building performance simulation (BPS) models continue to use very simple and rigid rules when a building's performance is predicted, despite the topic's complexity. This is likely an artifact of envelope load-dominated buildings, whose energy use is mostly dependent on their ability to isolate the indoor environment. But as envelopes and HVAC become more efficient, occupants are playing an increasingly important role on building performance; especially for highly efficient building (e.g., net-zero energy buildings). Traditionally the associated uncertainty of these effects has been excused for the designer and isolated during design by focusing on energy performance relative to a reference building. This paper proposes a method using a combination of probabilistic occupant models and explicit models of adaptive comfort to gain an improved understanding of robust building design. Results of an example of yield 45% lighting energy savings if a fixed shading device is present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".