Calibration and Validation of Climate-Based Daylighting Models Based on One-Time Field Measurements: Office Buildings in the Tropics
Bibliographic record
Abstract
Calibrated climate-based lighting simulation models of buildings have the capacity to perform an essential role in postoccupancy evaluations, such as annual frequency assessments of daylighting quality and visual discomfort. However, in most postoccupancy case studies the role of lighting analysis is temporally limited by instantaneous measurements or limited in scale by requiring constant monitoring with expensive sensors. It is challenging to build calibrated models based on point-in-time measurements due to the presence of electric lighting, transient use of dynamic shades, limited information on the material specifications, and short durations of accessibility to the spaces being studied. The authors propose and present a calibration process for annual daylighting and electric lighting simulation models based on one-time field measurements of large daylit and electrically lit spaces exemplified through a data set of 540 individual office desks across 10 office spaces. The calibration process includes measuring lighting, physical, and material data during a one-time visit that are used to calibrate high dynamic range images and lighting simulation models using actual weather data. The calibration accuracy is validated based on measured and simulated luminance and illuminance data. Comparing measured and simulated illuminance, relative root mean squared error (RMSE) values were 25.8% and 45.5% for horizontal and vertical measurements, respectively. When tracking errors using log10(illuminance), approximating human perceptual differences, errors of 4.3% and 6.8% were achieved. Vertical illuminance was found to vary more with measured data due to the uncertainty of monitor screen luminances. The authors aim to achieve calibrated lighting models that are reliable enough to be used in assessing the relationship of annualized lighting metrics to participants’ long-term perceptions of lighting quality, thereby enabling simulation models to be used in the postoccupancy evaluation process of building lighting. This article demonstrates that measured data through one-time visits can be utilized to build reliable calibrated lighting simulation models to integrate long-term annual lighting results in postoccupancy evaluations.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".