Analyzing the Benefits of Data Augmentation for Smart Grid Anomaly Detection and Forecasting
Bibliographic record
Abstract
In view of the escalating electricity demand and the pervasive implementation of electrical appliances, the safe and efficient operation of smart grids has been recognized as of significant importance. In recent years, machine learning has been widely applied to smart grid core applications, i.e., anomaly detection and electric load forecasting. In order to achieve precise anomaly detection and accurate time series forecasting, a significant quantity of historical data is usually required for model training. In practice, however, acquiring such a sizable dataset is often accompanied by high costs and many challenges, making it impractical in real-world tasks. In this work, we employ data augmentation techniques to expand the training set size for smart grid anomaly detection and time series forecasting tasks. Specifically, we investigate the efficacy of noise injection and a generative adversarial networks based augmentation method on various machine learning-based models. Extensive experiment results on two real-world datasets demonstrate the effectiveness of data augmentation techniques on anomaly detection and time series forecasting tasks. Experiment results show the benefits of data augmentation and provide guidelines for researchers and engineers.
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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".