Non-Intrusive Energy Use Monitoring for a Group of Electrical Appliances
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Energy use monitoring techniques are useful to home and facility owners, enabling them to save energy and reduce costs. The monitoring methods published so far are focused on detecting individual appliances and determining their respective energy consumption. This paper proposes a method for estimating the energy use of a group of appliances with similar electrical characteristics and operation patterns. The proposed method consists of two steps. The first step is to determine the probability that a given electrical event is caused by a member of the appliance group of interest. The second step analytically estimates the mean value and standard deviation of the total energy use of the appliance group based on the combination of Bernoulli distributions. Case studies using both simulations and field measurements have shown that the proposed method is able to accurately detect power and energy for a given group of appliances. It fills an important gap in non-intrusive energy use monitoring research for homes and facilities.
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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