A Very Deep One Dimensional Convolutional Neural Network (VDOCNN) for Appliance Power Signature Classification
Bibliographic record
Abstract
Estimating appliance specific power consumption using a single measuring device, known as Non-Intrusive Load Monitoring (NILM), is a challenging Blind Signal source Separation (BSS) problem. For the past two decades, numerous mathematical and pattern recognition techniques, including Fractional Hidden Markov Model (FHMM), Gaussian Mixture Model (GMM) and Mean Shift Based Clustering Techniques (MSBCT) have been proposed to decompose the total power consumption of a household into appliance specific power signals. The measurement sampling rate, operating characteristic of individual appliances and an unknown number of mixed signals create a big challenge in separating them. The main challenge is to design an algorithm that can learn appliance features accurately, before applying the algorithm to disaggregate the main power signals. To address this problem, A Very Deep One dimensional Convolutional Neural Network (VDOCNN) for appliance power signature classification is proposed in this research. As a first step, we have applied VDOCNN in learning appliance features from a given set of labeled training data. VDOCNN has achieved accuracy up to 98% in detecting appliance from its power signature using a UK Domestic Appliance-Level Electricity (UK-DALE) dataset. Using this algorithm, we are working towards disaggregation of power signatures for different appliances from a single power signal in future research.
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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 | 0.000 | 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".