Hierarchical safety control for micro energy grids using adaptive neuro-fuzzy decision making method
Bibliographic record
Abstract
In this paper a multi-level safety hierarchical control of a micro energy grid (MEG) is proposed. The MEG is mainly consisting electricity, heating and cooling energy systems which comprises renewable energy resources (i.e. photovoltaic (PV) and wind turbine (WT)) and thermal energy storage (TES). The majority of power electricity and heating are generated by co-generator (CG) gas turbine with assistance of renewable sources, which considered as eco-friendly gas emission as well as free energy production but on the other hand it has accompanied intermittency on energy production depends on varying weather conditions. This may affect the quality and reliability of the energy production and service if not properly controlled and coordinated. Therefore, to achieve an optimum and resilient performance of the micro energy grid, a hierarchical control is necessary and mandatory. A three level hierarchical control scheme for the MEG is offered with a use of adaptive-network-based fuzzy inference system (ANFIS).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".