Assessment of Non-Intrusive Load Monitoring as a Blind Source Separation Problem
Bibliographic record
Abstract
Non-Intrusive Load Monitoring (NILM) allows consumers to monitor appliances' power consumption without installing appliance-level sensors. NILM has become popular with the rapid deployment of smart energy meters. Neural network-based disaggregation approaches have provided more promising results in NILM. However, most of them must have labeled energy data for model training. The availability of energy data is inadequate, and the data must be updated frequently for higher disaggregation performances. However, none of the existing datasets is periodically updated. Therefore, training data has become a limiting factor for commercializing disaggregation solutions. This study addresses the issue by proposing an Independent Component Analysis (ICA) based solution to perform energy disaggregation as a blind source separation problem for the first time in the NILM domain. The aggregate energy signal has been used to prepare the independent input signals with a novel signal preprocessing approach. The results revealed the effectiveness of ICA in NILM applications.
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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 | 0.001 |
| Open science | 0.000 | 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".