Designing New Orthogonal High-Order Wavelets for Nonintrusive Load Monitoring
Bibliographic record
Abstract
This paper addresses the problem of load identification in nonintrusive monitoring application when using load signatures in transient signals. The study presents a systematic approach to design a set of wavelet filters to be used as matching patterns for load identification in nonintrusive monitoring. The effect of different higher order wavelet filters and the signal length on the classification accuracy are investigated. The concepts of wavelet clustering and wavelet-signal matching are introduced and are developed in this paper. Machine learning classifiers are used to automate the load classification process using co-testing. The introduced concept is evaluated on a real test system and the results of the experimental work have shown that 1.5 cycle may suffice to achieve a 97.25% classification accuracy. Moreover, the results have shown that the proposed approach is effective in detecting loads with more electronic components and not previously categorized in the assembly.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 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".