Energy Scheduling in IoE-Enabled Smart Grids Using Probabilistic Delayed Double Deep Q-Learning (P3DQL) Algorithm
Bibliographic record
Abstract
Decentralization and high penetration of smart devices in IoE-enabled smart grids face the power system with complex scheduling problems. Engaging with big data produced by the interconnected infrastructures, besides the high dimensional and uncertain environment, make traditional methods incapable of addressing these problems since exact modeling of the environment under uncertainties is impracticable. Also, learning-based methods suffer from excessive complexity and the curse of dimensionality. This research proposes a Probabilistic Delayed Double Deep Q-Learning (P3DQL) which is a combination of the tuned version of Double Deep Q-Learning (DDQL) and Delayed Q-Learning (DQL). The planned algorithm makes a trade-off between overestimation and underestimation biases guaranteeing efficiency regarding sample complexity and learning proficiency by applying a delay in updating the rule. Finally, the proposed algorithm is tested on real-world data from Pecan Street Inc., assessing the performance of the P3DQL regarding peak clipping, decreasing Peak to Average Power Ratio (PAPR), and cost reduction. The results indicate the superiority of the developed algorithm over other utilized methods by 28.2% peak clipping, 12.9% PAPR decrease, and 29.4% cost reduction.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".