On the Implementation of Two-Diode Model for Photovoltaic-Thermal Systems
Bibliographic record
Abstract
Photovoltaic (PV) cells are known for poor efficiency within the range of only 6-15%, depending on the type of cells. As a result, those can convert only a small part of the absorbed solar energy into electricity, the rest is wasted as heat, which also contributes to rise in cell temperature. This heating up is undesirable for PV cells because it further decreases the electrical conversion efficiency. One viable solution to this problem is the combination of PV cells with integrated thermal collectors, known as photovoltaic-thermal (PVT) collectors. This combination usually improves the PV module efficiency compared to stand-alone PV modules, because the fluid circulating underneath the PV cells removes the heat from the cells and cools them. Among the studies concerning PVT, Delisle [1] provided a good mathematical model, where the electrical output was calculated simply by considering a linear dependence of PV efficiency with cell temperature. In the current study, following the Delisle's approach [1] , a simple model configuration consisting of transpired collector absorber plate of corrugated type mounted underneath the PV cells is analyzed. The resulting mathematical system is solved numerically using multivariate Newton's method. To calculate the model output more accurately, a sophisticated model known as two-diode model is incorporated. This model provides current-voltage characteristics with maximum power point (MPP) tracker, and considers nonlinear temperature effect. The comparison of the model outputs with experimental data reveals that two-diode model behaves differently for different sets of data at different conditions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".