Cloud-based visual analytics for smart grids big data
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
Smart meters are a key technology to transfer information between service providers and end-users. However, the massive amounts of data evolving from smart grid meters used for visualization and control purposes need to be sufficiently managed to increase the reliability and sustainability of the smart grid. Interestingly, the nature of smart grids can be considered as a big data challenge that deals with huge amounts of data and their analytics. Therefore, this unprecedented smart grid data require an effective platform that elevates the smart grid in the big data era. This paper presents a visual analytics framework that can promote the sustainability of the smart grid. An application of the framework has been applied on a smart grid that contains over 6,000 smart meters for dynamic demand response visualization. Further, another application on data that includes micro-generators including an electrical vehicle is presented. The findings suggest that the framework is feasible in performing visual analytics and further smart grid data analytics.
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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 | 0.002 | 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