DESIGN METHODOLOGY FOR OPTIMIZATION OF ELECTRICITY GENERATION AND DAYLIGHT UTILIZATION FOR FAÇADE WITH SEMI- TRANSPARENT PHOTOVOLTAICS
Bibliographic record
Abstract
Semi-transparent photovoltaics (STPV) have a large potential for integration in fenestration systems, adding the option of solar electricity production while still allowing for satisfaction of daylight needs. This paper studies the potential of using such a technology and examines the impact of changing the photovoltaics (PV) area ratio (ratio of PV coverage to fenestration area) on the STPV façade. It includes a preliminary verification of the workplane illuminance model through comparison with measured data from an experimental office with a specially built fullscale prototype of a window with spaced solar cells in its upper section. The paper will address the issue of optimizing the PV area ratio for a simplified model based on a typical office in Montreal with a 3-section façade. The effect of changing orientation and PV efficiency on the overall net electricity generation (including the lighting load, heat gain from the artificial lighting, and the output of the PV) is presented. The annual simulation results show that a façade with integrated STPV has the potential to improve overall energy performance when compared with opaque PV due to the significant daylighting benefits even at low transparency ratios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".