Optimizing the Transaction Latency in the Blockchain-Integrated Energy-Trading Platform in the Standalone Renewable Distributed Generation Arena
Bibliographic record
Abstract
Renewable distributed generations are associated with generation intermittency. Exacerbated by the consumption and demand uncertainty and their resulting mismatch, its energy trading suffers similar uncertainty. The situation is severe in the standalone distributed generations (SDG) for lacking transaction access to the utility grid. This paper proposes the energy transaction time determination and minimization algorithm for consumers in the SDG arena. First, blockchain technology is adopted for transaction enhancement and transaction data acquisition. The acquired blockchain data includes the hourly nodes (number of blockchain members), transaction sizes, and corresponding transaction durations. Next, the blockchain-recorded transaction data are fitted using the linear regression (LR) algorithm to obtain their fitting formula. The fitting formula was subsequently optimized in hourly intervals to obtain the optimal transaction time (energy delivery time) using particle swarm optimization (PSO). Finally, the optimal results are presented in a decision tree (DT) to energy consumers in the blockchain platform. Consequently, their transaction decision-making is guided by the result against the inherent transaction time uncertainty. Consumers can thus correctly adjust their transaction habits to suitably adapt to the transaction duration fluctuations in the energy trading arena. Energy transaction delays and transaction costs are consequently minimized leading to greater penetration of renewables and bridging the generation and consumption gap.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".