Daylighting software validation study and development of a simplified method to predict the energy impacts of facade design and daylighting control in private offices
Bibliographic record
Abstract
Early design phase decisions can be critically important to the energy impact of a building. Building orientation and exterior aesthetics which drive window sizes and types are often made by architects and owners prior to the involvement of any daylighting or sustainability consultants. Because these designs can be difficult, if not impossible, to change as the design process proceeds, the need to inform and educate these decisions has led through this research to the development of a set of EnergyPlus based regression equations capable of predicting annual lighting, cooling, and heating loads of a private office design with minimal input. The ability to evaluate the impact of these three main energy consumption sources provides a complete picture of the consequences of design decisions that most other early design phase methods do not achieve. A test case using these equations calculated to within 4% to 8% of EnergyPlus simulation results.The development of the regression equations began with a validation study of four daylighting programs: EnergyPlus Detailed, EnergyPlus DELight, DAYSIM, and SPOT. Full scale daylighting measurements recorded in an empty private office in Ottawa, Ontario by the National Research Council of Canada provided data to validate the daylighting software against with EnergyPlus Detailed method selected for its accuracy and runtime. EnergyPlus was then used to perform parametric simulations of various building design parameters from which the regression formulas are created. These formulas are produced for four US cities of varying climates to confirm the regression approach is transferable.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 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".