Performance Evaluation of the ZIP Model-Phaselet Frame Approach for Identifying Appliances in Residential Loads
Bibliographic record
Abstract
This paper presents the analysis and development of a new approach to monitor and update the ON-OFF status of appliances in residential loads (RSLs). The proposed approach is structured to employ power meter readings P to determine the values for the magnitude |S̅| and phase θ of the apparent power. The value of |S̅|, associated with a value of P, is determined using Newton iterations, where a value of θ is calculated using six phaselet frames during each iteration. Once the iterations converge, the values of P and θ are used to construct the ZIP model (polynomial model) for the RSL, from which P is provided. The constructed ZIP model provides the values for the constants Kpfand Kqfthat relate the change in frequency to the active and reactive power demands of the modeled load. The obtained values of Kpfand Kqfare compared to standardized values that are defined for each appliance in an RSL. The ZIP model-phaselet frame approach is implemented as an algorithm for monitoring the ON-OFF status of appliances in RSLs. The algorithm for the proposed approach is developed without a need to collect data for training. Test results show simple implementation, good accuracy, and insensitivity to variations in energy 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".