Fuzzy Electricity Management System with Anomaly Detection and Fuzzy Q-Learning
Bibliographic record
Abstract
Smart grid management is an emerging research topic that recently has adopted artificial intelligence algorithms to assist in the task. However, as more and more data is used, data insecurity and cyber-physical attacks hinder the performance of intelligent systems. In this paper, we propose a fuzzy electricity management system (FEMS) consisting of an attention-based anomaly detection module for attack classification and a fuzzy Q-learning decision module for grid management. Experimental results show that the proposed anomaly detection module achieves high accuracy and F1 scores, significantly outperforming state-of-the-art systems. As for the management evaluation, FEMS achieves extremely low convergence days and mean absolute error (MAE) of supply distribution, which proves the effectiveness of the proposed FEMS in shaping supply distribution. Moreover, FEMS achieves the lowest failure rate (highest stability) but a slightly higher MAE of operating reserve rate due to the unavoidable trade-off between grid stability and energy efficiency.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".