A Data-Driven Method to Detect the Abnormal Instances in an Electricity Market
Bibliographic record
Abstract
Participants in an electricity market expect to have a fair, transparent, and open competition. Market Surveillance Administrators (MSA) are responsible for monitoring the market outcomes to investigate if they are consistent with the fundamentals of the electricity markets. It can be an immensely time-consuming process with high amounts of computations in an electricity market with huge numbers of participants. Besides, a manual review of market operations may be biased by involving humans in the decision making process. If this anomaly detection procedure can be done automatically then it can be a great aid to the market surveillance process for having an unbiased and prompt tool to monitor the market. In this paper, an anomaly detection algorithm is proposed to identify the events of interest in an electricity market. This algorithm provides the MSA with a tool to detect the instances in the electricity market when electricity price behavior deviates from the normal expected regime. These anomalous hours can then be analyzed further in order to diagnose the reason.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".