A fuzzy AHP and GIS-based approach to prioritize utility-scale solar PV sites in Saudi Arabia
Bibliographic record
Abstract
Determining site suitability for utility-scale solar PV power plants requires complex decisions. Basing such decisions on extensive information, especially from the geographical information system (GIS), offers significant advantages such as improved project performance, minimized power loss, and reduced environmental impacts. The primary aim of this research is to evaluate the ideal location for utility-scale solar PV projects using the GIS combined with a Fuzzy Analytic Hierarchy Process (AHP) in the country of Saudi Arabia. Various economic and technical factors are considered in the proposed model and are ranked using a fuzzy AHP approach. The best selection for solar PV is a tradeoff between maximum power achievement and minimal project cost. An analysis of land suitability is computed to classify the suitability area into three different categories: "high," "moderate", and "low". The results obtained from the analysis show that 15% and 8.2% of the study areas show high and moderate suitability levels, respectively. By considering only 10% of the highly suitable land, the potential power generated per year could reach 8,330,807 GWh, which is 28 times greater than the current energy sales in Saudi Arabia, which is around 294,612 GWh/year.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".