ANFIS Based Energy Management System for V2G Integrated Micro-Grids
Why this work is in the frame
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Bibliographic record
Abstract
Description and evaluation of an adaptive neuro-fuzzy inference system (ANFIS) based energy management system (EMS) for a vehicle-to-grid integrated micro-grid is given in this paper. A grid-tied micro-grid with a wind turbine and a photovoltaic solar panel as primary energy sources, and an energy storage system based on electric vehicle (EV) batteries is considered in this study. The ANFIS-based supervisory controller determines the power that must be generated by or stored in the EV batteries, taking into account the power demanded by the micro-grid and available EV power considering the battery state of charge, rated capacity, and time remaining for departure of the EVs. The Sugeno based ANFIS EMS is compared with a Mamdani based fuzzy EMS, thus evaluating two different artificial intelligence approaches for solving the same power allocation problem. Dynamic simulations demonstrate that the ANFIS based EMS is able to allocate power optimally among available resources during various uncertainties simulated in the system and is also able to provide a better power allocation when compared to the fuzzy based EMS.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it