Deployment of Multiple Demand Response Programs Using Data-Driven Multi-Step Method with Elasticity
Bibliographic record
Abstract
Assessment and qualification of consumers' behavior play an important role in the selection of the suitable Demand Response Programs (DRPs) achieving their engagement and satisfaction. Targeting the assessed consumers' clusters with adequate and elastic pricing schemes ensures their participation. The model offers the full visibility on the dynamicity of pricing and demands for the various simultaneous clusters' DRPs operating in a synchronized manner. The originality of this paper resides in the classification of consumers' behaviors according to the criteria and the elasticity of the multiple dynamic offered pricing schemes for the clusters along with their impacts on each other. An optimal solution is reached through assessment, qualification, planning, benefits' visibility on various proposals and relocation. Intensive what if scenarios assist in the decision making process of the optimal selection in a planned phase. Thus, contracts' terms and conditions suiting the consumers and the electrical utility are arranged. The method is validated through a simulation on Matlab using clustering and multi-objective optimization.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".