Mining Energy Consumption Behavior Patterns for Households in Smart Grid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Inarguably, buying-in consumer confidence through respecting their energy consumption behavior and preferences in various energy programs is imperative but also demanding. Household energy consumption patterns, which provide great insight into consumers energy consumption behavioral traits, can be learned by understanding user activities along with appliances used and their time of use. Such information can be retrieved from the context-rich smart meters big data. However, the main challenge is how to extract complex interdependencies among multiple appliances operating concurrently, and identify appliances responsible for major energy consumption. Furthermore, due to the continuous generation of energy consumption data, over a period of time, appliance associations can change. Therefore, they need to be captured regularly and continuously. In this paper, we propose an unsupervised progressive incremental data mining mechanism applied to smart meters energy consumption data through frequent pattern mining to overcome these challenges. This can establish a foundation for efficient energy demand management while ameliorating end-user participation. The details and the results of evaluation of the proposed mechanism using real smart meters dataset are also presented in this paper.
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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 | 0.000 |
| 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 it