Incremental mining of frequent power consumption patterns from smart meters big data
Bibliographic record
Abstract
The key elements for understanding power consumption of a typical home are related to the activities that users are performing, the time at which appliances are used, and the interdependencies with other appliances that may be used concurrently. This information can be extracted from context rich smart meters big data. However, the main challenge is how to mine complex interdependencies among different appliances usage within a home where multiple concurrent data streams are occurring. Furthermore, generation of energy consumption data from a smart meter is an ongoing continuous process and over period of time inter-appliance associations can change or new ones can establish. In this paper, we propose incremental mining of frequent power consumption patterns from smart meters big data. Our model exploits the benefits of pattern growth strategy and mine in quantum of 24 hour period, i.e. frequent patterns are extracted from data comprising of appliance usage tuples for 24 hours period, in a progressive manner. The details and the results of evaluating the proposed mechanism using real smart meters dataset are presented in this paper.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".