Non-invasive Load Identification Based on Real-Time Extraction of Multiple Steady-State Parameters and Optimization of State Coding
Bibliographic record
Abstract
Drawing on the principles of non-intrusive load monitoring (NILM) and the load parameter detection function of smart remote load controller (SRLC), this paper presents a non-intrusive load identification method based on real-time extraction of multiple steady-state parameters and the optimization of state coding. Firstly, the characteristic parameters of loads were extracted by the Intelligent Power Management Platform, and the original load data were clustered by the improved affinity propagation (AP) algorithm, creating a sample set of multiple steady-state parameters. Considering the working states of loads, a load decomposition model was established, and the objective function was optimized by genetic algorithm (GA), realizing the decomposition and re-identification of household loads. Finally, our method was proved to have an accuracy of over 96% through experiments. The research results provide a reference for electricity department to identify the type and features of loads on the consumer side.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".