Load Decomposition at Smart Meters Level Using Eigenloads Approach
Bibliographic record
Abstract
The deployment of the advanced metering infrastructure (AMI) in distribution systems provides an excellent opportunity for load monitoring applications. Load decomposition can be done at the smart meters level, providing a better understanding of the load behavior at near-real-time. In this paper, loads' current and voltage waveforms are processed offline to form a comprehensive library. This library consists of a set of measurements projected onto the eigenloads space. Eigenloads are basically the eigenvectors describing the load signatures space. Similar to human faces, every load has a distinct signature. Each load measurement is transformed into a photo and an efficient face recognition algorithm is applied to the set of photos. A list of all the online devices is always stored and can be accessed at any time. The proposed method can be implemented at the smart meters level. The distributed computation that can be achieved by performing simple calculations at each smart meter, without the need for sending intensive data to a central processor, is beneficial. From a system operator perspective, load composition in near-real-time provides the loads' voltage dependence that are needed, for example, in volt-VAR optimization (VVO) in distribution systems. Further applications of load composition data are also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 0.003 |
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".