Optimized performance of PV panels and site selection for a solar park in Pakistan
Bibliographic record
Abstract
Information such as the optimal tilt and azimuth angle of the solar collectors’ location is needed to efficiently incorporate solar energy. Also, when building a solar park, it is important to know the most suitable and high-energy generating location, thus saving time and money. This study analyzed collector geometry for Karachi in particular and Pakistan in general. Karachi has the potential of 339.36 kWh·m−2·annum−1 at a fixed annual tilt of 26°. In case the collector geometry needed to be changed for Karachi, we adopted a range of 40° azimuth angle and 20° tilt angle from its maximum value. The difference in power produced with these modifications would only be 1%. Optimal yearly and monthly tilt for most of the potential solar park locations of Pakistan (300+) were calculated, and we found that the optimal tilt for Pakistan closely follows the value for latitude. Generally, changing the tilt angle monthly is recommended for areas that produce more energy, whereas a fixed annual tilt could be suitable for low energy-producing regions. The effects of temperature were also incorporated while examining the energy produced by the photovoltaic (PV) panels.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".