Control strategies for lighting and ventilation in offices: effects on energy and occupants
Bibliographic record
Abstract
Participants (N=126) spent a day in a full-scale office laboratory, completing questionnaires and standard office tasks. Some participants experienced typical constant lighting and ventilation conditions, whereas others were given personal control over the dimming of lighting in their workstation and over the flow rate of air from a ceiling-based nozzle in their workstations. Half of the participants, some with personal control and some without, were exposed to environmental changes typical of demand—response load shedding in the afternoon: workstation illuminance was reduced by 2% per minute, and ambient air temperature increased by ∼1.5°C over a 2.5 hour period. Results showed that personal environmental control improved environmental satisfaction. Personal control over lighting led to an average energy reduction of around 10% compared to a typical fixed system; participants with personal control also reduced flow rate compared to the constant condition. Use of each control type averaged two—three control actions per person per day, which dropped to less than one control action per person per day in a longer-term pilot study (N=5) conducted in the same space. Load shedding had some small negative effects for occupants, but in practice is unlikely to create substantial hardships, and is a reasonable response to peak power emergencies.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".