A Decision–Making System for Managing Renewable Energy Alternatives and Strategy Using Triangular Neutrosophic Bipolar Fuzzy TOPSIS
Bibliographic record
Abstract
With the rising global population and the urgent need to reduce reliance on nonrenewable energy, selecting optimal renewable sources is critical. Among these, solar energy stands out due to its wide availability, scalability, and minimal environmental impact. This study proposes a novel triangular neutrosophic bipolar fuzzy TOPSIS (TNBF-TOPSIS) model that prioritizes solar energy by integrating both desirable criteria (e.g., eco-friendliness, economic viability, and operational stability) and undesirable aspects (e.g., intermittency, land use, and regional constraints). Unlike previous methods, our approach introduces a new averaging mechanism for positive and negative attributes using the triangular neutrosophic bipolar fuzzy Einstein hybrid aggregation (TNBFEHA) operator, enhancing the precision and robustness of multi-criteria group decision-making. Distances from ideal solutions (TNBF-PIS and TNBF-NIS) are computed to reduce subjectivity. The model is applied to evaluate solar, wind, hydropower, and geothermal energy sources, with findings highlighting solar energy as the most suitable option. Beyond energy planning, the framework holds potential for applications in environmental policy, sustainable urban development, and smart grid design. This study offers a comprehensive and distinguished tool for decision-making under uncertainty, reinforcing the centrality of solar power in achieving sustainable and resilient energy systems.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".