On Delay Sensitivity Clusters of Microgrid Data Aggregation Under LTE-A Links
Bibliographic record
Abstract
Accurate analysis and classification of delay sensitivity of the data sent from smart meters is a two-fold challenging problem which contains analytics and network latency dimensions. In the case of resilient community microgrids, vitality of the energy consumption and power usage data is further evident so to make proactive decisions to entail smooth transitions between islanded and non-islanded modes of the microgrids. In light of these, this paper analyzes smart microgrid data aggregation against time intervals to determine the delay sensitivity of aggregated messages sent over microgrid networks via LTE and LTE+links. To meet the latency requirements, we propose a Time of Use (ToU)-aware and unsupervised learning-backed microgrid data aggregation scheme to cluster the message of the same delay sensitivity and prioritize bursts with respect to delay sensitivity to achieve low delay overhead for high and moderately delay sensitive messages. Through simulations, we have shown that by using ToU-aware model, microgrids can prioritize 247% more critical consumption requests in order to keep stable operations during islanded mode.
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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".