Customizable Preference Models for Prosumers in Peer-to-Peer Energy Trading
Bibliographic record
Abstract
The transition toward decentralized energy systems necessitates advanced mechanisms for optimizing energy-sharing frameworks and dynamic supplier-consumer matching that aligns energy transactions with individual preferences. To this aim, this paper introduces a preference-driven approach for supplier-consumer matching in peer-to-peer (P2P) energy-sharing systems, addressing limitations in previous models that fail to capture the diversity and dynamism of consumer preferences. The proposed method introduces three customizable preference models and employs a multi-objective optimization model to evaluate suppliers based on critical attributes: cost, energy assurance, and security. Experimental findings validate the robustness of the proposed approach, demonstrating its ability to efficiently rank suppliers and accommodate consumer preferences across diverse scenarios involving large supplier pools and multiple attributes. The approach proves adaptable to varying consumer demands, balancing computational efficiency with responsiveness to consumer needs. The results underscore the potential of this approach to enhance energy-sharing systems by enabling more personalized and scalable supplier-consumer interactions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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".