Explainable Robust Smart Meter Data Clustering for Improved Energy Management
Bibliographic record
Abstract
The widespread deployment of smart meters in residential settings has led to a wealth of high-resolution electrical power consumption data, providing the opportunity to discover valuable insights into energy consumption patterns. Mixture models are essential for revealing hidden patterns in data, enabling accurate insights and informed decision-making across diverse applications. In this paper, we introduce the mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions (BAGGUMM) and investigate its potential for characterizing residential energy users, thereby enhancing energy management applications, including demand response and energy efficiency programs. We investigate the potential for improved clustering efficacy by incorporating an inner mixture containing the Uniform distribution to enhance robustness against outliers. Additionally, we integrate a decision tree algorithm for model explainability to define pattern boundaries using if-then statements. We validate our proposed model using three real-life datasets. Additionally, The performance of BAGGUMM is compared against several state-of-the-art mixture models.
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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".