Lightswitch : a model for manual control of lighting and blinds
Bibliographic record
Abstract
A simulation algorithm and its implementation into three building simulation programs are presented. The algorithm predicts the lighting energy performance of manually and automatically controlled electric lighting and blind systems in perimeter daylit offices. Algorithm inputs are annual profiles of user occupancy and work plane illuminances. These two inputs are combined with probabilistic switching patterns, which have been derived from field data, in order to predict the status of the electric lighting and blinds throughout the year. Assumptions underlying the model are currently being validated and refined through a series of field studies which are carried out within the International Energy Agency's Task 31, Daylighting Buildings in the 21st Century. The algorithm has been linked to three simulation programs:- Lightswitch Wizard: An online design support service (www.buildwiz.com) for daylighting and lighting control related design decisions in perimeter offices.- Daysim: An advanced, Radiance-based version of the Lightswitch wizard that allows to model arbitrary building geometries (http://www.nrc-cnrc.gc.ca/eng/projects/irc/daysim.html).- Esp-r: In order to yield more holistic energy predictions, the integration of the model into the Esp-r whole building simulation engine is currently underway.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".