Power Appliance Disaggregation Framework Via Hybrid Hidden Markov Model
Bibliographic record
Abstract
The recent proliferation of the Internet of Things (IoT) has provided consumers unprecedented connectivity and access to many of their devices via mobile applications and smart home energy management systems. Although many platforms are available for consumers to remotely fine-tune their energy consumption patterns, awareness is still lacking of specific details about their past consumption trends. When contextual data is presented regarding specific appliances that were active in the past along with associated costs, consumers will be incentivized to make power consumption decisions that result in increased cost-savings and energy conservation. In this paper, we propose a hybrid classification system based on the Hidden Markov Model (HMM) and k-Nearest Neighbours (KNN) algorithms for classifying and disaggregating power consumption data of individual households in a non-intrusive manner. We also apply the Pareto's 80/20 Principle for accurate identification of appliances that draw significant power and contribute to majority of energy costs.
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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) | 0.001 | 0.001 |
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".