Daylight and Lighting Optimization in Passive House Design: Case Study of a 3D-Printed Building in Cold Climate
Bibliographic record
Abstract
This paper presents a practical workflow to integrate daylight and electric-lighting optimization into Passive House design. The approach links facade orientation, window-to-wall ratio, glazing selection, and exterior shading with quantitative lighting targets and whole-building primary energy accounting. Using a cold-climate multifamily case study in Amos (Canada), the method combines daylight autonomy and glare assessments with PHPP energy balance and specifies LED systems with daylight-linked dimming and occupancy control. Results indicate that a south-dominant glazing strategy—paired with moderated east and west exposures, selective glazing with balanced visible transmittance and solar factor, and exterior shading—reduces lighting electricity use and summer peaks while preserving winter solar gains. The proposed workflow is repeatable from early design through verification and shows that lighting, often treated as a secondary fit-out, is a primary lever for meeting Passive House primary-energy limits without compromising visual comfort.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".