Modified Differential Golomb Arithmetic Lossless Compression Algorithm for Smart Grid Applications
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Bibliographic record
Abstract
The advancement of the electric grid has led to tremendous growth in data generated from the installed sensors. Efficient storage and transmission of this data pose a challenge for the utilities. Thus, it is required to have a data compression technique to reduce the data size. There are state of the art compression algorithms that can be applied to reduce the amount of data for storage and transmission in the smart grid environment. Some of these algorithms exploit characteristics of the load profile data, where consecutive data samples have very small differences. However, performance of these algorithms deteriorate when there are frequent large differences. We propose a modification that improves compression performance when there are large value differences. The algorithm is evaluated on smart meter load profile data at different data resolution. We show that the proposed changes improve performance by 2-20% for different resolutions.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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