Testing the Performance of Bus-Split Aggregation Method for Residential Loads
Bibliographic record
Abstract
This paper presents the implementation and performance testing of the bus-split method for aggregating power demands of energy storage appliances (i.e., heating units and water heaters) in residential loads. The tested method is based on modifying the bus-split formulation to replace load models with the power demands of constant-impedance and constant-power (PZ and PS) load components. The values of PZ and PS are determined by the ZIP phaselet method, which can provide PZ and PS for each reading of the household power meter. The combination of the ZIP-phaselet method and bus-split aggregation can eliminate the need for measuring the power demands of individual energy storage appliances, thus, simplifying the implementation of the the bus-split aggregating of residential loads. The bus-split method has been implemented for performance evaluation using data collected from 20 households during the fall, winter, spring, and summer seasons. Performance results show that the developed aggregation method can provide accurate, simple, and nonintrusive aggregation of the power demands for energy storage appliances. Moreover, test results show that the bus-split method has minor sensitivity to the type and/or ratings of aggregated appliances, along with negligible sensitivity to seasonal variations of household power demands.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".