Demand-Side Management Using Deep Learning for Smart Charging of Electric Vehicles
Bibliographic record
Abstract
The use of electric vehicles (EVs) load management is relevant to support electricity demand softening, making the grid more economic, efficient, and reliable. However, the absence of flexible strategies reflecting the self-interests of EV users may reduce their participation in this kind of initiative. In this paper, we are proposing an intelligent charging strategy using machine learning (ML) tools, to determine when to charge the EV during connection sessions. This is achieved by making real-time charging decisions based on various auxiliary data, including driving, environment, pricing, and demand time series, in order to minimize the overall vehicle energy cost. The first step of the approach is to calculate the optimal solution of historical connection sessions using dynamic programming. Then, from these optimal decisions and other historical data, we train ML models to learn how to make the right decisions in real time, without knowledge of future energy prices and car usage. We demonstrated that a properly trained deep neural network is able to reduce charging costs significantly, often close to the optimal charging costs computed in a retrospective fashion.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".