DESIGN OF A RISK MODEL AND ANALYTICAL DECISION INFORMATION SYSTEM FOR POWER OPERATION IN THE CONTEXT OF SMART GRID, 1-9.
Bibliographic record
Abstract
With the increasing requirements of society for energy conservation and emission reduction, electricity is seen as an important energy supply method to promote energy conservation and emission reduction.The study combines hierarchical analysis, rough set theory and fuzzy comprehensive evaluation method to propose a new power system operation effectiveness assessment method based on improved fuzzy hierarchical analysis.The study uses Institute of Electrical and Electronics Engineers Power & Energy Society (IEEE PES) Power System Test Cases Data Set, Power System Analysis Toolbox and GridLAB-D Test Cases as the objects of the study.The distribution is more distinctive and hierarchical.The results show that after the application of the model proposed by the research institute, the overall generation efficiency has been significantly improved.All sampling times have exceeded 85.5%, and most of them are concentrated at about 88%.At the same time, the proposed model runs only 22.17 s, which is more efficient, and the overall correlation is as high as 0.97097.The fit degree is very high, which proves high training accuracy.Overall, this study contributes to the development of smart grid technology and the improvement of power system operation and management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".