A Data-Driven Approach for Generating Synthetic Load Patterns and Usage Habits
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
Today's electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the Internet of Things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g., appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.
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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