Switchable Building-Integrated Photovoltaic–Thermal Curtain Wall for Building Integration
Bibliographic record
Abstract
This study presents a novel switchable multi-inlet Building integrated photovoltaic/thermal (BIPV/T) curtain wall system designed to enhance solar energy utilization in commercial buildings. The system integrates controllable air inlets and motorized dampers that dynamically adjust airflow patterns in response to real-time environmental conditions such as solar irradiance, ambient air temperature, and PV panel temperature. A steady-state energy balance model, developed using a thermal network analogy and implemented in Python, was used to simulate winter operation in Montréal, Canada. Three operating modes with different air inlet configurations were assessed to evaluate system performance across variable air velocities and solar conditions. Results indicate that the switchable system improves combined thermal and electrical generation by 2% to 25% compared to fixed one- or two-inlet systems. Under low irradiance and air velocity, one-inlet operation is dominant, while higher solar gain and airflow favor two-inlet configurations. The system demonstrates effective temperature control and enhanced energy yield through optimized airflow management. This work highlights the potential of integrated control strategies and modular façade design in improving the efficiency of solar building envelope systems and offers practical implications for scalable deployment in energy-efficient, heating-dominated climates.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".