Optimizing Microgrid Efficiency through Edge Computing: A Comparative Analysis with IoT Technologies
Bibliographic record
Abstract
Microgrids are considered one of the technologies capable of promoting energy sustainability and resilience. Related Work Microgrid systems are widely monitored and managed based on IoT. Despite this fact, IoT-based solutions are also getting restricted more and more with high latency and restricted processing power as the microgrid becomes more complex. This paper aims to circumvent these computational inefficiencies and maximize the overall system efficiency. This paper examines the use of Edge computing in the microgrid. The study measures and compares the latency, data rate, and energy efficiency between Edge computing and IoT via a microgrid testbed. As observed in the actual experiment, the latency can be significantly reduced and the data processing efficiency significantly enhanced by edge computing compared with the traditional way of IoT. Through the support for flexible and efficient microgrid operation, edge computing contributes to the optimal utilization of resources and the stability of the grid. The findings of the study indicate that edge computing offers a promising alternative to address the limitations of classic IoT to deliver more advanced and intelligent microgrid management strategies. Accordingly, this paper provides useful insights for researchers, practitioners, policy makers, and planners to enhance the performance and penetration of a microgrid with advanced technology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".