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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".