Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes
Bibliographic record
Abstract
Household demand response (DR) is an important research problem that aims to modify consumer’s energy consumption. One of the promising areas is clustering Appliance Operation Modes (AOMs) and inducing DR by promoting consumption patterns that use less energy-intensive modes. This work proposes a novel clustering approach (DDTWSC) which aims to cluster AOMs based on the similarity of the appliance load profiles (SUPs). DDTWSC leverages the power of the DensityBased Spatial Clustering of Applications with Noise (DBSCAN) algorithm to partition the appliance load profiles into clusters of similar profiles that share the same AOM. Within DBSCAN, to measure the similarity between SUPs, the Dynamic Time Warping (DTW) algorithm is used. The resulting clustering is evaluated against two publicly datasets, namely RAE and UK-DALE. The Silhouette score is used to measure the performance of the proposed technique in clustering SUPs. DDTWSC demonstrated a significant improvement in the results compared to similar previous work in the literature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".