Blockchain-Based Privacy Preserving and Energy Saving Mechanism for Electricity Prosumers
Bibliographic record
Abstract
With the development of distributed and renewable energy resources and smart grids, energy management systems that allow electricity prosumers to schedule their power usage, are seen as a prominent solution for reducing electricity costs. This paper presents a novel blockchain-based mechanism to incentivize prosumers to save energy, while preserving their privacy. In the proposed mechanism, each prosumer utilizes an energy management system that is based on the percentage power change (PPC) at each hour of the day. The use of PPC values allows the proposed blockchain to preserve the privacy of the prosumers as no sensitive information is shared. The calculated PPC values are shared among the prosumers. The prosumer with the minimum PPC value is selected as the validator of the blockchain, which is responsible for creating the next block of the blockchain. The problem of approving the validator, by other prosumers, is formulated using a novel zero-metric weighted average consensus. The communication model required to reach this consensus is investigated and the consensus value is analytically derived. Multiple test systems with varying number of prosumers are simulated and analyzed. The results demonstrate the capability of the proposed mechanism to sustain energy while preserving privacy in a scalable manner.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".