Multi-criteria decision making approach for solar energy implementation using N-cubic fuzzy interaction aggregation operators
Bibliographic record
Abstract
The demand of electrical energy has developed considerably as the population growth and automation in factories are still increasing. The fast changing market has resulted in the intensive increase in the manufacturing activity that has further increased the energy demands of the production activities. Solar power has therefore come up as potential remedy to this demand. Nevertheless, in case of manufacturing companies, the process of choosing, installing, and maintaining appropriate solar panel systems is a complicated one particularly when several criteria and different hierarchies of decision-makers are concerned. Further, each phase in the lifecycle of a solar panel system, such as analysis, installation, operation and decommissioning should be addressed with much care. This means that the manufacturers of solar panels should have total answers to every step. In such real-life scenarios, the multiple attribute group decision-making (MAGDM) is an effective decision-making tool to deal with uncertain and imprecise information. Aggregation operators (AOs), which are some of the tools in MAGDM that are most commonly studied, can be of specific use in the integration of different types of evaluations. We consider the theory of N-cubic fuzzy sets (NCFSs) and its fundamental operations in this work, as well as introduce a new type of aggregation operators N-cubic fuzzy interaction aggregation operators (NCFIAOs) to describe the relations between different expert opinions in uncertainty. Based on the NCFS framework, the following specific averaging operators are proposed: the NCF interaction weighted average (NCFIWA), NCF interaction ordered weighted average (NCFIOWA) and NCF interaction hybrid weighted average (NCFIHWA), each having its operational laws. To illustrate the usefulness of the proposed operators in a real-life situation, we use them to approach a real solar panel selection problem, which is one of the areas of critical concern in the national energy policy and sustainable development. One of the numerical examples will be used to describe the decision-making (DM) process, and the comparative analysis with the existing AOs will prove the effectiveness, strength, and competitiveness of the suggested approaches.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".