Selection of Potential Sites for Sustainable Development of Solar Photovoltaic Plants in Northeastern Brazil Using GIS and Multi-Criteria Analysis
Bibliographic record
Abstract
Brazil has one of the highest percentages of solar radiation in the world and which it is a favorable condition to generate electricity using solar photovoltaic systems. The construction of photovoltaic plants depends on the site selection that influences the electricity generation capacity and the socio-economic benefits that can be derived from it in the future. This article proposes to combine Spatial analysis that involves the acquisition and management of spatial data using a Geographic Information System (GIS), and the fuzzy multi-criteria AHP-TOPSIS model to identify potential locations for the installation of solar photovoltaic systems in the northeastern region of Brazil, State of Pernambuco. The combination of GIS and fuzzy AHP-TOPSIS offers the user the possibility of assigning merit categories to the mapping according to multiple assessment criteria. The GIS include factors of the restrictions and criteria. The restrictions are inserted into the GIS using layers defined from current legislation (urban areas, undeveloped land, community sites, infrastructure, etc.), which reduce the study area by eliminating zones in which PV installation is not permitted. The results show that 22 and 40% of the area of the state of Pernambuco has the very high and high potential, respectively, for the implantation of PV.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".