A novel demand side management program using water heaters and particle swarm optimization
Why this work is in the frame
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Bibliographic record
Abstract
Power systems' operators have the task of maintaining the balance between the demand and generation of electric power. Much research and attention is being given to find more environmental friendly sources of power generation. Naturally, more power is required when the load is at its peak value, and this tends to be when the most non environmentally friendly sources of power generation are used. This paper proposes a new controller for peak load shaving by intelligently scheduling power consumption of domestic electric water heater using binary particle swarm optimization. Past studies show that similar demand side management programs were not successful because the impact that the load control has on the end users' comfort. In this study, Binary Particle Swarm Optimization (BPSO) finds the optimal load demand schedule for minimizing the peak load demand while maximizing customer comfort level. A simulation in Matlab is used to test the performance of the demand response program using field data gathered by smart meters from 200 households. The direct load control is shown to be an effective tool for peak shaving of load demand, shifting the loads to valleys and reducing the aggregated load of electricity without compromising customer satisfaction.
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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