A Data-driven Assessment Model for Demand Response Participation Benefit of Industries
Bibliographic record
Abstract
Demand Response (DR) is an incentivized program by the utility operator to provide an opportunity for consumers to play a significant role in the electric grid operation by shifting or reducing loads during peak periods. This work proposes a data-driven methodology that only uses smart meter data to identify load flexibility in industrial loads of consumer and cost-saving potential from participating in a DR program. The first step of the methodology involves an unsupervised clustering of historical demand loads data based on$K$-means algorithm to identify the energy usage behavior of an industrial consumer. An operation demand flexibility boundary is then calculated from the identified clusters. These boundaries are the flexible region where demand load ramp-up and ramp-down can be are achievable. Two DR participation scenarios (i.e., Passive and Active DR participation) based on Linear Constrained Optimization are designed where optimal daily electrical demand trajectory under DR participation scenario is estimated to evaluate the net benefit of DR participation. The case study of an electronics factory indicates that 4% – 7% monthly net benefit can be achieved from passive DR participation, and 14% – 19% monthly net benefit can be achieved from active DR participation. This methodology provides industrial consumers with a non-intrusive assessment of electrical load flexibility potential and associated DR participation benefit without going through the physical onsite audit process.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".