An Online Smart Microgrid Energy Monitoring and Management System
Bibliographic record
Abstract
Energy demand has shown an increase in the recent years, and distributed energy generation and load management systems are essential components in modern MicroGrids (MG). An effective and continuous monitoring of the grid represented a challenge, an online evaluation is necessary to improve the generation and load distribution performance. This paper presents an energy saving and management design strategy based on Fuzzy Logic Control in a residential grid-connected AC microgrid. A control strategy based on human reasoning aimed to reduce the grid power fluctuation, and improve battery lifecycle. In this system, PhotoVoltaic cells "PV", Inverter, and Max Power Point Tracing MPPT are used in addition to a battery bank. The proposed method regulates the power flow of the microgrid, improved the Load-management performance. Experimental studies are carried out to test and validate the proposed system using an online Fuzzy-Logic regulation with multiple Loads and Battery-charge conditions. Results have shown a recognized improvement in the grid fluctuations profile as function of load/power generations. This is also expected to enhance/improve the instantaneous grid power balance and demands response.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".