DESIGN OF A RISK MODEL AND ANALYTICAL DECISION INFORMATION SYSTEM FOR POWER OPERATION IN THE CONTEXT OF SMART GRID, 1-9.
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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