Residential Household Non-Intrusive Load Monitoring via Graph-Based Multi-Label Semi-Supervised Learning
Why this work is in the frame
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Bibliographic record
Abstract
Nonintrusive load monitoring refers to inferring what appliances are operating in a household at a given time solely from fluctuations on the main power feeder. It is one approach to demand-side management strategies in the smart grid, and most current research employs machine learning to make those inferences. However, the learning algorithms used usually require knowledge of the set of appliances active at each sample instant in addition to the main feeder fluctuations. This data is not ordinarily available in field usage. As a compromise, we examine semi-supervised learning algorithms, which only need a small sample of observed power signals annotated with active appliances (e.g., from an initial “registration” period). As multiple independent appliances may operate concurrently, we furthermore employ multilabel classification in our solution. Three new graph-based semi-supervised multilabel load monitoring algorithms are proposed and evaluated on five public datasets. We find that the best algorithm can outperform state-of-the-art results on these datasets.
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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 | 0.001 | 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 it