Performance Evaluation of the ZIP Model-Phaselet Frame Approach for Identifying Appliances in Residential Loads
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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 K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pf</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qf</sub> that relate the change in frequency to the active and reactive power demands of the modeled load. The obtained values of K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pf</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">qf</sub> are 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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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