Agent-based simulation of home energy management system in residential demand response
Bibliographic record
Abstract
This paper presents an agent-base model to evaluate the home energy management system in residential demand response implementation. Residential demand response aims to change people's electricity consumption patterns to reduce the peak demand and therefore improve energy efficiency and power system stability. The home energy management system intelligently controls household loads with association of smart meters. It plays key roles in a success demand response implementation. In the proposed agent-based model, the main stakeholders are modelled by the software agents including Conventional Home Agents, Smart Home Agents, a Utility Agent, a Primary Plant Agent and Secondary Plant Agents. A mechanism of dynamic pricing is applied to both the Conventional Home Agent System (Scenario #1) and the Smart Home Agent System (Scenario #2). Comparing to the Scenario #1, the peak demand, average householder's bills and generation cost in the Scenario #2 is decreased by 24.6%, 7.4% and 14.7% respectively. This demonstrates the effectiveness of the home energy management system in the residential demand response implementation. The proposed model can be a test-bed to evaluate various demand response strategies and technologies.
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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) | 0.001 | 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) | 0.004 | 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".