Real-Time AI-Driven EV Charger Load Management for Residential Electrification
Bibliographic record
Abstract
We presented an Electric Vehicle Supply Equipment Load Management System (EVLMS) that enables the integration of EVSE into current residential infrastructures without the need for utility service upgrades or structural changes. The proposed EVLMS operates locally, proactively managing EVSE loads in response to the overall load and phase balance of the entire residential building. The EVLMS acts as a gateway for EVSEs and utilizes native Wireless Smart Utility Network (Wi-SUN) to communicate with AMI 2.0-enabled metering systems along with Modbus (RS485) for rooftop solar inverters. This solution not only promotes affordable electrification in homes but also ensures balanced energy loads, which reduces energy losses and contributes to decarbonization efforts. The results and findings show that the model effectively tackles important challenges in EVSE integration, such as dynamic load balancing, optimal use of renewable energy sources, and adherence to feeder constraints.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".