Open Source Software-Based Industrial Internet of Things for Aiding Visibility to Distributed Generation
Bibliographic record
Abstract
Distributed generation (DG) and digitization drive the recent global energy transition. Digitization helps bring more visibility to the grid and makes better understanding and management possible. Unfortunately, DGs are often located far from the utility control center, and it is difficult to extend the SCADA networks to the edge level. Moreover, the customer-owned DGs support vendor-specific monitoring apps, and their access is limited to utility. To address this issue, this article proposes an open-source software-based edge data collection and visualization framework. The approach is entirely vendor-agnostic and best fits with distributed energy resources (DERs) located remotely, even with intermittent network connectivity. The framework is demonstrated by considering two DER nodes that are using the Modbus protocol. The data is successfully aggregated from the edge and visualized at the cloud level. The application of open-source tools has great potential in addressing the issues of such a large heterogeneous power system network.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".