Using an overlay network to manage the renewable energy in residential areas
Bibliographic record
Abstract
We propose a P2P overlay network that works as a communication infrastructure for the applications of Smart Grid to monitor and control the intelligent electric devices in Power Grid. This paper also includes a case where the power flows in the residential areas are managed by a mechanism implemented on top of this overlay network. The peer nodes of the overlay network are the computers. These computers construct into an overlay network that has the structure of the R-tree (used by some spatial databases for indexing its objects) through mapping to the tree nodes. They take over the intelligent devices in the geographic area around their own locations by communicating with them using the devices' protocols. In our case study, these peer nodes run the procedures of the energy management mechanism to manage the power flows in their geographic areas. Our simulation results show that, using the overlay network, the energy generated by the distributed generators is effectively shared by the homes at the different times of day. The paper also present the simulation results showing the properties of the overlay network, including the scalability in terms of the stretch of the path between a peer node and the control center. For the case of churn, the experiment results indicate that the number of split or merge depends on the parameters that the overlay network has.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".