Semantic modeling in the construction of digital twins of energy objects and systems
Bibliographic record
Abstract
The article deals with the problem of building Digital Twins and Smart Digital Twins for control and management in power systems. The energy system is understood as a set of energy resources of all types, methods for their production (extraction), transformation, distribution and use, as well as technical means and organizational complexes that ensure the supply of consumers with all types of energy. Integrated intelligent energy systems are analyzed as one of the important trends in the Russian energy sector, and the main directions of digitization of the energy sector are considered. The concept of "digital twins" in technical fields is considered as one of the main digitalization trends, an ontological approach to building digital twins and semantic models for building smart digital twins are proposed. It is proposed to use a fractal approach when performing ontological engineering, which makes it possible to formalize the concepts of the subject area and allows you to build different-scale ontologies using metalevels of ontologies. Formalized models of digital twins and smart digital twins are presented. The developed approaches are illustrated by the example of construction of digital twins of a solar power plant and smart digital twins of a fuel and energy complex. The approach described in the article makes it possible to integrate different levels of digital and smart digital twins into a single digital solution when modeling energy facilities and power systems.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".