Developing a model for predicting optimum daily tilt angle of a PV solar system at different geometric, physical and dynamic parameters
Bibliographic record
Abstract
Capturing the solar radiation that passed through the Earth’s atmosphere and received by solar panels depends on several parameters. In this paper, all governing parameters of the total daily solar radiation are provided in mathematical relations. In the first stage, the mathematical model is converted to a computer-based model by using the MathCAD program. In the second stage, however, the control variables, tilt angles, surface azimuth angles, day of year, and ground reflectance are identified. Based on the total daily solar radiation objective function, three scenarios are proposed in this study for different situations of variation of control variables. In the final stage the optimization flowchart is designed for the optimum daily tilt angle. The model’s innovation to simultaneously analyze the mean effects of control variables on the dynamic and optimum tilt angles simulation was designed based on 3 scenarios for a PV solar system of the building in Montreal in different seasons. After analyzing the correlation among the scenarios, values of the average optimum tilt angles for each scenario are simulated in angle ranges of 60° to 65° for the winter, 20° to 22.5° for the spring, 27.5° to 35° for the summer, and 68° to 75° for fall.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 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".