Self-Adaptive Evaluation of Hybrid AC/DC Distribution Networks with Multi-Energy Complementary Systems
Bibliographic record
Abstract
The hybrid AC/DC distribution networks with multienergy complementary systems (HDN-MECS) have become a powerful supporting platform for the vertical integration of single energy and the horizontal integration of multiple energy. However, the complex composition, diverse operating scenarios, and strong spatiotemporal coupling have all posed major challenges for the comprehensive evaluation of HDN-MECS operating effectiveness. To this end, a self-adaptive evaluation framework is proposed in this paper based on the unique characteristics of HDN-MECS. Taking maximum entropy criterion (MEC) as an objective function, this paper proposes a two-stage interval analytic hierarchy process (IAHP), and solves the problem with a hybrid algorithm of improved particle swarm optimization (IPSO) and chaos optimization (CO). A self-adaptive interval analytic hierarchy process (SAIAHP) is developed to weight performance indicators under the framework, which aims to enable dynamic, robust evaluation considering human expert inputs. A case study forecasting verifies the accuracy and applicability of the SAIAHP method.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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 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".