Wavenilm: A causal neural network for power disaggregation from the\n complex power signal
Bibliographic record
Abstract
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by\nestimating individual appliance power usage from a single aggregate\nmeasurement. Deep neural networks have become increasingly popular in\nattempting to solve NILM problems; however, many of them are not causal which\nis important for real-time application. We present a causal 1-D convolutional\nneural network inspired by WaveNet for NILM on low-frequency data. We also\nstudy using various components of the complex power signal for NILM, and\ndemonstrate that using all four components available in a popular NILM dataset\n(current, active power, reactive power, and apparent power) we achieve faster\nconvergence and higher performance than state-of-the-art results for the same\ndataset.\n
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".