Modelling Building-Integrated Photovoltaic/Thermal Systems: Sensitivity Analysis Insights
Bibliographic record
Abstract
Transformation of buildings to solar-powered structures can support the evolution of the global energy network towards a decentralized and environmentally sustainable system. Building-integrated photovoltaics thermal (BIPV/T) systems can play a crucial role in this process by facilitating the decarbonization of the heating sector through electrification. By generating both electrical and thermal energy from the same surface area their overall efficiency is improved making them particularly advantageous in dense urban environments where space is limited. The main design parameters which influence the BIPV/T energy performance include geometric features of the system such as channel length and height, inclination, and orientation as well as control parameters such as the air flow rate that runs through the BIPV/T channel.In this study a sensitivity analysis on selected design parameters of a BIPV/T system coupled to an air-source heat pump is conducted to identify sensitive features that have the biggest impact on the system performance.Key findings include that longer BIPV/T channels heighten the importance of channel height in the thermal production of the BIPV/T system, while higher channel air velocities generally decrease the impact of geometric parameters. Finally, the analysis indicated that there is a critical range of channel heights and lengths within which the sensitivity of these parameters on the outlet temperature of the BIPV/T channel is maximized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".