Applying Geographic Information System (GIS) for Solar Power Plants Site Selection Support in Makkah
Bibliographic record
Abstract
Solar energy is one of the most important components of renewable energy, which constitutes an important source of clean energy in many fields, especially water desalination and electricity generation. With the increase in electricity consumption in the Kingdom of Saudi Arabia at an annual rate of 5%, the National Initiative for the production of water and electricity was launched. The current study aims to apply a multi-standard GIS method to determine the most appropriate spatial sites for solar energy collection in the Makkah Administrative District. A set of conditions and criteria have been relied upon, whether planning criteria, environmental criteria, or an environmental criterion, to obtain a digital appropriate model that shows the best sites for constructing solar power plants. The study determined the required criteria by relying on literary studies and creating a digital geographic database for these requirements, and then integrating these requirements into an integrated geographic information system in order to obtain a spatial fit model. The results of the suitability indicate that all areas of Makkah Al-Mukarramah are suitable for the solar energy project with an appropriate percentage ranging between 30% and 80%. These results are promising for the renewable energy sector in Makkah Al-Mukarramah and should be taken into consideration. By analyzing these spatial sites and their degrees of suitability to standards, it was found that the lands that are characterized by an adequate share of more than 80% have an area of about 4000 square kilometers with a percentage of 3% of the total suitable lands. These highly suitable areas are concentrated on the governorates of the Makkah Al-Mukarramah Administrative Region, where the Taif governorate comes in the first place with 35% of the total area, followed by the two governorates of Turbah with 24%, and the Rania governorate with 14%. A digital map was made showing the spatial distribution of suitable lands for solar energy projects in the Makkah Al-Mukarramah Administrative Region. The study recommended applying the obtained results in the national plan for renewable energy sources in the Kingdom of Saudi Arabia.
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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 | 0.001 | 0.001 |
| 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".