Electric lighting energy savings for a photocell controlled dimmer - a comparative simulation study using DOE.2 and Lightswitch Wizard
Bibliographic record
Abstract
This paper suggests a method how to predict electric lighting energy savings for photocell controls in daylit spaces taking manual blind control into account. The method predicts lower but supposedly more 'realistic' lighting energy savings than conventional simulation approaches. On the other hand, predicted savings are larger than those predicted by ASHRAE 90.1 power adjustment factors. The method is demonstrated in a hypothetical open plan office in Calgary, Canada. A comparative simulation study is presented using DOE.2 and the RADIANCE-based daylighting analysis tool DAYSIM. Simulation results were found to differ substantially between DOE2 and DAYSIM even in the absence of venetian blinds. These discrepancies were attributed to the underlying simulation algorithms and sky models. Despite these differences, both simulation programs yielded energy savings more than twice the static 10% allowed for by ASHRAE 90.1 power adjustment factors for on/off control.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".