Management of PHEV charging from the smart grid using sensor web services
Bibliographic record
Abstract
Plug-in Hybrid Electric Vehicles (PHEVs) have less fossil- fuel dependency, lower carbon emissions and lower operating costs than the conventional vehicles. On the other hand, wide adoption of PHEVs is expected to increase the load on the power grid. Sensor web services have recently emerged as promising tools that can provide remote management, data collection and querying capabilities for the sensor networks in the smart grid. In this paper, we use sensor web services for management PHEV charging in order to increase the administration ability of the utility over load and increase the control of the consumer on her energy expenses. In our application, the driver can remotely access the State of Charge (SOC) of her vehicle, provide a destination address and query cost and emissions for alternative fuel options. Moreover, the application communicates with the utility web services, learns the advertised critical peak periods and avoids charging the vehicle during those periods to protect the grid. We show that our scheme reduces driver expenses and vehicle emissions while providing an acceptable SOC level. In addition, we show that the application is efficient in terms of completion time and the code sizes are suitable for a sensor node.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".