A Stacked Ensemble of Attention-Augmented Deep Learning Models for Robust Anomaly Detection in Smart Grids
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
The detection of anomalies in smart meter electricity data represents a critical task for ensuring power grid stability and security, an endeavor imperative for grid modernization. This undertaking is complicated by complex temporal patterns, seasonality, and subtle irregularities that challenge conventional detection methods. To address this, a novel attention-augmented deep ensemble framework is proposed. The methodology involves the independent training of four diverse neural architectures-a Bidirectional Long Short-Term Memory (BiLSTM), a Gated Recurrent Unit (GRU), a Temporal Convolutional Network (TCN), and a Transformer model-all enhanced with attention mechanisms to capture a wide spectrum of temporal dependencies. The individual predictions are subsequently integrated via a stacking ensemble utilizing an eXtreme Gradient Boosting (XGBoost) meta-learner. By leveraging the unique strengths of recurrence, convolution, and self-attention, the hybrid architecture achieves superior anomaly detection capabilities. Empirical evaluations and ablation studies on public energy consumption data demonstrate that the framework attains a state-of-the-art F1-score of 0.572 and an AUC of 0.927, significantly outperforming individual baseline models and other deep learning architectures. The result is a practical and reliable solution for intelligent energy monitoring that combines interpretability with robust performance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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