A Decision–Making System for Managing Renewable Energy Alternatives and Strategy Using Triangular Neutrosophic Bipolar Fuzzy TOPSIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 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 it