Load Shifting Agents for Automated Demand Side Management in Micro Energy Grids
Bibliographic record
Abstract
This paper describes a novel approach for the automated management of micro energy grids. In particular a market based resource allocation mechanism is used to control energy generators and consumers within a micro energy grid. This approach requires energy consumers (producers) to buy (sell) their energy demands (supplies) through a specialized electronic auction platform. But as manually negotiating all energy demands and supplies on such a market is a tedious task, its automation is highly desirable and thus leads to the main contribution of this paper: The automation of the demand side bidding process through electronic bidding agents, which are equipped with an intelligent buying strategy that allows them to dynamically react to market changes and adapt their bidding behavior accordingly. More precisely, the agents are able to shift energy demand within certain boundaries from (expensive) peak hours to those times of the day where energy demand and thus energy prices are lower in order to minimize their cost. Moreover, as our results show, this behavior leads to a smoothed load curve for the whole system, i.e. demand peaks are reduced while base load increases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".