Evaluating, Modeling and Predicting of the Differential Consumption Profiles for Residential Customers Subscribed to Dynamic Pricing Tariffs
Bibliographic record
Abstract
Intermittent renewable energy sources and end-use electrification such as transportation and heating introduce a significant challenge for the reliability of the power grid. In order to deal with this important challenge, utilities put in place different demand-side management mechanism such as demand response programs and dynamic tariffication (DT) to shift electricity consumption outside peak period. To meet new needs and to respond to changes in the energy market, Hydro-Quebec undertakes an important research project, named SCÉNARIO, to simulate the impact of the different customers’ load profiles on the network. Dynamic pricing is one of the components of SCÉNARIO project which aimes to assess the impact of customer behavior on the distribution network during demand management. In this paper, we propose an algorithm for evaluating, modeling and predicting of the differential consumption profiles for residential customers subscribed to dynamic pricing tariffs. These kinds of investigations allow the power system planners to evaluate and predict the impact of the dynamic pricing programs on the power system behavior.
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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 | 0.000 |
| Open science | 0.001 | 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 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".