Efficient Incremental Smart Grid Data Analytics
Bibliographic record
Abstract
Analytical computations over energy data are gaining popularity thanks to the growing adoption of smart electricity meters. Computations in this context range from seemingly straightforward tasks such as calculating monthly bills based on time-of-use pricing, to elaborate building for predictions and recommendations in an eort to reduce peak demand. While research in this promising area is progressing steadily, published algorithms and prototypes have largely avoided the important practical question of how to deal eciently with the incremental nature of energy data, for example per-hour readings produced by smart electricity meters. As a stepping stone towards a comprehensive solution to this problem, we investigate incremental techniques for disaggregating dierent categories of energy consumption, such as base load versus activity load, from hourly smart meter data using the popular \three-line model of Birt et al. Our software prototype, called Insparq, exhibits speedups in excess of 2x for data sets up to tens of GB in size, compared to a naive implementation on top of a conventional scalable batch processing framework.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".