Estimation of Global Solar Radiation and Optimal Tilt Angles of Solar Panels for Pune, India
Bibliographic record
Abstract
Solar panels can achieve the highest yield when they are oriented in the right way as the influence of the tilt angles can lead to a high impact on the performance. This paper demonstrates the effect of the tilt angle on the final yield and maximum utilization of solar energy. The annual optimal tilt angle of the solar panels for Pune was found out using the Liu and Jordan model. The optimal tilts yielding an increase of 7% was found out to be 0° on the months starting from April and lasting till September and 40° starting from October and holding at 40° till March. By changing the tilt angle every month, there was an increase in the yield by more than 8%. For better yield and maximum utilization of solar energy, optimizing the tilt angles of solar panels monthly or even twice a year is effective based on the study. Most of the panels are fixed at an angle equal to the latitude of the place and oriented towards the Southern sky in the northern hemisphere. Measurements of the monthly average global solar radiation, monthly average temperatures, and monthly average relative humidity were compared. Further ahead regression correlation for a linear model was carried and the regression coefficients a and b were found out to be 0.2804 and 0.3618 respectively.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".