Minimizing the Utilized Area of PV Systems by Generating the Optimal Inter-Row Spacing Factor
Bibliographic record
Abstract
In mounted photovoltaic (PV) facilities, energy output losses due to inter-row shading are unavoidable. In order to limit the shadow cast by one module row on another, sufficient inter-row space must be planned. However, it is not uncommon to see PV plants with such close row spacing that energy losses occur owing to row-to-row shading effects. Low module prices and high ground costs lead to such configurations, so the maximum energy output per available surface area is prioritized over optimum energy production per peak power. For any applications where the plant power output needs to be calculated, an exact analysis of the influence of inter-row shading on power generation is required. In this paper, an effective methodology is proposed and discussed in detail, ultimately, to enable PV system designers to identify the optimal inter-row spacing between arrays by generating a multiplier factor. The spacing multiplier factor is mathematically formulated and is generated to be a general formula for any geographical location including flat and non-flat terrains. The developed model is implemented using two case studies with two different terrains, to provide a wider context. The first one is in the Kingdome of Saudi Arabia (KSA) provinces, giving a flat terrain case study; the inter-row spacing multiplier factor is estimated for the direct use of a systems designer. The second one is the water pump for agricultural watering using renewable energy sources, giving a non-flat terrain case study in Dhamar, Al-Hada, Yemen. In this case study, the optimal inter-row spacing factor is estimated for limited-area applications. Therefore, the effective area using the proposed formula is minimized so that the shading of PV arrays on each other is avoided, with a simple design using the spacing factor methodology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".