Research on Microgrid Power Direct Transaction Behavior Based on Blockchain
Bibliographic record
Abstract
Because data mining has a good processing capacity for data, the clustering analysis of the historical data which is direct transactions of microgrid power provides a reliable technical support. In order to solve the problem of the imbalance between the power demand of users and the power supply of the grid in the direct transaction of microgrid power, an algorithm based on spectral clustering combined with empirical rules is proposed in this paper. The historical data (such as electricity energy, quotation submission time, and transaction price) between users and power suppliers who complete transaction settlement through the blockchain is used for clustering analysis by this method. By analyzing the clustering results, a reliable adjustment scheme to control the balance of supply and demand in microgrid power market is obtained. Through simulation experiments, the feasibility of the direct power trading model based on blockchain and the effectiveness of the algorithm based on spectral clustering combined with empirical rules are verified, so as to obtain the variation law of electricity demand and electricity price in different time periods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".