Comparative Analysis of Prediction Accuracy from Daylighting Simulation Tools
Bibliographic record
Abstract
In this article, a validation analysis of predictions from selected daylighting simulation capabilities is presented. The daylighting simulation tools considered in the study include EnergyPlus Detailed, EnergyPlus DELight, DAYSIM, and SPOT (Sensor Placement and Orientation Tool). The National Research Council of Canada provided detailed site and sensor data for an experiment recording daylight exposure throughout a side-lit private office space using various daylighting strategies. The site information provided included geometry of the space and daylighting test cases through detailed SketchUp models. Reflectivity and transmittance properties of experiment surfaces and windows were well documented to enable accurate replication in daylighting models. On-site weather data were also collected to allow the test environment to be repeated. Illuminance measurements at 5-min intervals at 12 sensor locations within the space allowed computer simulations to be thoroughly tested. The three daylighting test cases chosen for the validation study, no daylighting device, interior light shelf, and exterior horizontal blinds, were selected to test each software’s ability to model direct sunlight, interior obstructions, and exterior reflections. Through graphical observation and statistical measures DAYSIM was determined to be the most accurate overall. However, it is important for users to consider the intended application of the daylighting software selected. Depending on the daylighting conditions, facade construction, and sensor location, other software performed equally well. Each of the studied software also has other features such as photosensor modeling, integration with thermal simulations, and occupancy predictive models that must be weighted when selecting the appropriate daylighting software for your application.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".