Electricity Theft Detection of Residential Users With Correlation of Water and Electricity Usage
Bibliographic record
Abstract
Electricity theft users with zero electricity usage (UZEU) should be specifically concerned in electricity theft detection (ETD) research. The challenges are: they provide no effective information on electricity usage behaviors, and they are easily confused with vacant house users. This has caused the majority of the existing detection methods relying on single electricity usage to fail to identify UZEU accurately. Hence, this article first analyzes the underlying correlation between water and electricity (W&E) usage collected by the smart meter. This analysis then lends the theoretical basis to propose a new ETD method by comprehensively using the multisource information. More precisely, the proposed method utilizes the mutual information coefficient (MIC) to construct a correlation model between W&E usage and in turn the wavelet clustering algorithm to cluster the MIC of the power distribution users. Thereafter, the resulting weak correlations indicate the suspected users as the electricity theft UZEU in case of zero electricity usage. Finally, the proposed method is validated by numerical experiments in the real world and illustrated to be more accurate than existing methods in detecting UZEU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".