Co2 Emissions Reduction of Photovoltaic Thermal Model Using Variable Flow Rate Values
Bibliographic record
Abstract
In this paper, Co2 emission reduction of Photovoltaic Thermal (PVT) model using variable flow rate values has been examined and evaluated for seventy-three different Photovoltaic (PV) coverage area cases of PVT (between 20% to 80 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). A data set of weather conditions of a city in Canada over one year is used for this study. Co2 emission reduction is investigated for each month for the different cases in this work. For minimizing Co2 emissions, the percentage of PV coverage area values for each month is determined. To maximize the annual Co2 emission reduction, specific PV coverage area values are carefully chosen for each month using variable flow rate. Results show that the annual Co2 emission reduction can be maximized by using adapted (dynamic) PV coverage area values compared to the conventional (static) PV coverage area values of the PVT system during a year. The fitted-curve function is obtained which evaluates the Co2 emissions reduction for each month at different PV coverage area ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".