Unlocking the potential energy savings from shorter time delay occupancy sensors
Bibliographic record
Abstract
Linking occupancy sensors to switches or dimmers is a well-established strategy yielding substantial lighting energy savings. Indeed the technology is considered proven enough that it is now mandated for many space types in most commercial buildings via all major energy codes. Current codes typically require a maximum 30-minute timeout or delay following the last detected occupancy before switching lights off. New codes may reduce this timeout to 20 minutes, promising more savings. It has been long recognized that reducing timeouts further, to 5 minutes or less, will deliver even greater savings [1, 2]. So, why aren’t energy codes more aggressive in mandating shorter timeouts? There are two principal answers to this: (1) prevailing sensor technology is not reliable enough to detect occupancy at such short timescales; (2) even if the sensors were reliable, shorter timeouts would mean more frequent switching, potentially reducing the lifetime of fluorescent lamps to an uneconomical degree. In this article we re-evaluate the potential energy savings of shorter timeout periods with two datasets, and discuss how such timeouts might be viable with LED technology.
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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".