Generative Artificial Intelligence for Panoramic Sensing and Prediction Techniques in Novel Power Distribution Systems
Bibliographic record
Abstract
This paper is based on the definition of novel distribution system panoramic perception technology under the perspective of generative artificial intelligence.The preprocessed data are put into forward GRU neurons and reverse GRU neurons as model input variables for multi-task assisted training, and the model outputs distribution system perception results to complete the task of constructing a new distribution system panoramic perception model based on BiGRU.When the distribution system current and voltage data is zero, it will lead to a reduction in the current and voltage prediction accuracy of the distribution system of the ELM model, for this reason, it is proposed to use the genetic algorithm to optimize the ELM model, to achieve the modeling of the new distribution system prediction model based on the ELM-GA algorithm.Using the model constructed in this paper, panoramic perception and prediction analysis of the new distribution system is carried out.When the BiGRU model is deployed in the new distribution system, the BiGRU network's system perception accuracy and error rate are 95.00% and 5.00%, respectively, which fully meets the user experience requirements of the new distribution system, and the relative errors of fault voltage and fault current prediction based on the ELM-GA algorithm for the new distribution system are less than 5%, which indicates that the ELM-GA distribution system prediction model has the characteristics of high robustness and high accuracy.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".