Effects of different environmental and operational factors on the PV performance: A comprehensive review
Bibliographic record
Abstract
Abstract Conventional fossil fuel‐based power generation is one of the main contributors to global environmental pollutions. The rapid depletion of fossil fuel reserves as well as their adverse environmental impact heighten the quest for cleaner and sustainable energy resources to generate electricity. Solar energy is an unlimited and immeasurable source of renewable energy that is used for direct electricity production through the solar PV cell. However, environmental conditions as well as operation and maintenance of the solar PV cell affect the optimum output and substantially impact the energy conversion efficiency, productivity and lifetime, thus affect the economy of power generation. In this study, an investigation about recent works regarding the effect of environmental and operational factors on the performance of solar PV cell is presented. It is found that dust allocation and soiling effect are crucial, along with the humidity and temperature that largely affect the performance of PV module. Additionally, the wind itself carries a significant amount of dust and sand particles, especially in the deserted areas. Deposition of dust in humid conditions forms adhesive, sticky mud on the PV cell and worsens the situation as it reduces the power generation up to 60–70%. This study discusses advanced approaches to mitigate the effects of these factors with their relative merits and challenges. Finally, a guideline is proposed to minimize the effect of different environmental and operational factors to optimize the performance of solar PV cell.
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 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) | 0.000 | 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".