User-Aware Game Theoretic Approach for Demand Management
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Bibliographic record
Abstract
Demand-management programs intend to maintain supply-demand balance and reduce the total energy cost. In this paper, we propose a user-aware demand-management approach that manages residential loads while taking into consideration user preferences. Maximizing users' savings and comfort can be two contradicting objectives. We identify a trade-off between these two objectives and propose an energy consumption optimization model, as well as a game theoretic approach to take this trade-off into account. User comfort is modeled in a simple yet effective way that considers waiting time, type of appliance, as well as a weight factor to prioritize comfort over savings. The proposed game is based on a modified regret matching procedure and borrows advantages of both centralized and decentralized schemes. Through simulations, we show that the proposed approach is scalable, converges in acceptable times, introduces a very limited amount of overhead in the system, achieves very high cost savings, and preserves users' preferences. Extensive simulations are used to evaluate the performance of the optimization model and the proposed approach.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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