An Improved MCDM Model to Support Smart Energy Management System in Smart Grid Paradigm
Bibliographic record
Abstract
Smart grid development is required to accommodate the integration of renewable generation resources into systems. Energy management is improved by smart devices that allow two-way communication. A multi-criteria decision analysis framework is used in this study to provide a model for decision making. Prior to communicating with the energy market aggregator, it considers the human-oriented viewpoint to offer an interface with a smart device within smart grid system. In this work, for decision-making optimization, the proposed framework has utilized while considering six criteria and have evaluated across three multi-criteria decision-making techniques. To assess criteria, a thorough investigation has been undertaken across five load classes aiming at demand side management (DSM) options from high load class to lowest load class and concerned load-generation balance. The findings of this research make it convenient for the framework that communicates the actual results of the energy system and the energy markets aggregator for an energy management plan. The results have been evaluated with sensitivity analysis across five DSM options and trade-off studies have been carried out usefulness in aiding effective decision-making procedures in practice. Finally, this research offers a cost-effective, clean, and effective system configuration aimed at consumer preferences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".