A grey-box modelling methodology for liquid-based building integrated photovoltaic/thermal collectors
Bibliographic record
Abstract
This paper presents the validation and calibration of a grey-box modelling methodology for a liquid-based PV/T collector. A 1st order simplified thermal network model was developed and calibrated utilizing PV/T experimental testing data from a large-scale solar simulator in Concordia University, Canada. The grey-box model was then evaluated with multiple datasets with varying parameters including solar irradiance, surface wind speed, and mass flow rate in quasi-steady-state conditions. The average overall relative error stayed below 1.69% for all test cases while the maximal relative error between the model and the experimental measurements was 1.52% for the PV/T electrical production and 4.78% for the fluid outlet temperature. The grey-box methodology has shown that a PV/T model can be calibrated utilizing only the electrical production of the PV/T and the inlet/outlet temperatures to achieve high-accuracy prediction of the electrical and thermal performance. The study found that the largest impact on the error between the model and the experimental data was found to be the effect of wind speeds and mass flow rates.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".