Synthetic Power Consumption Data Generation For Appliance Operation Modes
Bibliographic record
Abstract
Realistic appliance power consumption data plays a pivotal role in the development of smart home energy management systems and the foundational algorithms for appliance data analysis. However, publicly available datasets are often limited in availability and time-consuming to collect. Consequently, the creation of simulation models for generating synthetic appliance data becomes needed. In this research, a novel approach is designed to simulate power consumption data that is tailored towards appliance operation modes. This model leverages existing public datasets and employs stochastic methods to enhance data variability and consistency. Usage profile characteristics are extracted and used to generate base usage profiles. To synthesize usage profiles while ensuring realism, a probabilistic model is employed and tuning parameters, encompassing components such as white noise, switch-on surges are added to refine the synthetic profiles. The DTW algorithm is then utilized to assess the proximity of the synthetic profiles to the existing ones. Remarkably, our results reveal that the average differences among these profiles can be as low as ten samples, even with a 1Hz sampling frequency.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".