Research on Decentralized Energy Trading Mechanism and Carbon Footprint Traceability Based on Heterogeneous Blockchain and Federated Reinforcement Learning for Interactive Energy Markets
Bibliographic record
Abstract
Energy, as one of the larger contributing industries to greenhouse gas emissions, has an urgent task to reduce emissions, and standardizing the carbon footprint and trading mechanism of the energy market is an important concern for the development of the current energy industry.Under the guidance of the principle of green, low-carbon and sustainable development of the energy market, this paper first uses heterogeneous blockchain and federated reinforcement learning to design a decentralized energy trading mechanism model.It is found that the model fails to realize the intelligent detection and control of carbon footprint, in this regard, on the original model, the carbon footprint origin algorithm is introduced.Combining the above models and algorithms, the current interactive energy market is explored and analyzed.Consumer user 5 has the largest net benefit, with a specific value of 15.05 million yuan, and comprehensive energy supplier 3 has the largest net benefit, with a value of 37,467,000 yuan, indicating that this paper's model implements the principle of green, lowcarbon and sustainable development of energy while meeting the energy needs of consumers and suppliers, maximizing the interests of each other in the process of energy trading, which proves that this paper's research has excellent practical application value.
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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 | 0.003 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".