Flexibility platform for community energy systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrating technological changes and sustainability considerations poses multidisciplinary challenges for the power system beyond economic and environmental benefits. Allowing energy from distributed energy resources to be traded and coordinated peer-to-peer in real-time can mitigate system and policy-making issues while decreasing the strain on power system infrastructure. Transactive Renewable Energy Exchange (TREX) is artificial intelligence (AI)-assisted flexibility platform for community energy systems that can also act as an AI training tool. Using AI agents to manage instantaneous market interactions in real-time is the first step to long-term sustainability and flexibility. In this article, the authors show that deep learning agents are able to learn to exploit the trading habits of opposing expert-designed traders in a TREX environment. Based on the results, future efforts will be extended towards a multi-agent setup with full utilisation of the capabilities of the market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 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 it