Building Climate-based Daylighting Models Based On One-time Field Measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Calibrated climate-based lighting simulation models of buildings perform an essential role in postoccupancy evaluations (POE), such as annual frequency assessments of daylighting quality and visual discomfort. However, the role of lighting analysis is temporally limited by instantaneous measurements or limited in scale by requiring constant monitoring of occupied spaces with expensive sensors. Building calibrated models is thus challenging due to limited information, short durations of access, the concurrent presence of electric lighting and daylighting, and transient usage of dynamic shades of occupied spaces. In this paper, the authors present a calibration process to build annual daylighting and electric lighting simulation models based on one-time field measurements, exemplified through a dataset of 540 individual office desks across 10 office spaces. The authors calibrated lighting models to be reliable enough for assessing the relationship of annualized climate-based daylighting metrics (CBDMs) to participants long-term perceptions of lighting quality. The proposed process to build calibrated climate-based models for POE’s based on one-time field measurements at each building is validated through comparing measured and simulated illuminance data at every work desk and results are sufficiently positive with logarithmic relative RMSE values of 4.3% and 6.8% and relative RMSE values of 25.8% and 45.5% for horizontal and vertical illuminances respectively. Vertical illuminance was found to vary more with measured data due to the uncertainty of monitor screen luminances. This paper demonstrates that measured data through onetime visits can be utilized to build reliable calibrated lighting simulation models to integrate long-term annual lighting results in post-occupancy evaluations.
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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 | 0.001 | 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 | 0.001 |
| 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 it