Optimization of the BIPV Ventilated Facade
Bibliographic record
Abstract
One of the main challenges and opportunities for PV is building integration. Integration of PV in buildings can be considered for many purposes besides electricity generation and a holistic approach to building design and operation will lead to the greatest economic and environmental benefits. Ventilated building facades offer an excellent opportunity to increase use of the solar resource with the integration of a BIPV array into the facade. The Photovoltaic Energy Applied Research Lab (PEARL) at the British Columbia Institute of Technology (BCIT) developed and constructed a Building Integrated PV ventilated facade for the retrofit of a building in downtown Vancouver, Canada. The new facade enhances the thermal performance of the building envelope by controlled ventilation of the air space between the two facades with DC fans powered by BIPV modules built into the new building facade. Performance of the ventilation system is enhanced by the use of a specially developed controller, which uses maximum power point tracking and voltage control to optimize the performance of the PV array. Multidisciplinary expertise in mechanical, structural, electrical and electronic engineering was required and the technical viability of such an architectural approach to the structural integration of PV is presented.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".