Bibliographic record
Abstract
Demand-responsive buildings utilize control mechanisms to reduce their electricity use during periods of high grid-wide demand, primarily to aid utilities in maintaining grid stability. Dimming lighting is proposed as one such demand response mechanism, and several laboratory studies have explored the speed and extent of dimming that is either noticeable or acceptable to occupants. We conducted a field study to examine whether these laboratory findings could be applied in real buildings with commercial lighting control systems. The study, conducted during summer months, included an open-plan office with 330 dimmable luminaires, and a college campus with 2300 dimmable luminaires across several buildings. In the office building we conducted two afternoon demand response trials, which dimmed lights by up to 35 percent over 15–30 minutes. The power reduction achieved was 5.2 kW (23 percent), and 5.3 kW (24 percent), respectively. At the campus site we conducted three afternoon demand response trials, which dimmed lights by up to 40 percent over 1–30 minutes. The power reduction achieved was 15.2 kW (18 percent), 7.7 kW (14 percent), and 11.3 kW (15 percent), respectively. There were no lighting-related complaints to facilities management throughout the afternoons of the trials. Based on prior laboratory studies and this field study we suggest guidelines for dimming lighting as a demand response strategy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".