Combining electric vehicle and rechargeable battery for household load hiding
Bibliographic record
Abstract
The transition from electromechanical to advanced smart meters offers great opportunities in load management, energy saving, and resource optimization. However, the fine-grained usage data collected by the smart meters also raises privacy concerns. The household load profile can be analyzed by non-intrusive load monitoring techniques to infer customer activity routines and behavioral preference. Several data obfuscation techniques based on controlling a local rechargeable battery or a controllable load have been proposed to mitigate the privacy leakage of the customers. In this paper, we introduce the use of electric vehicles (EVs) for load hiding. In particular, we propose an EV-assisted battery load hiding algorithm, which combines the use of an EV with a local rechargeable battery to achieve the dual purpose of optimal charging and measurement obfuscation. Since EVs are becoming increasingly popular and part of many households, their use renders the implementation of load hiding less costly compared to methods only using dedicated rechargeable batteries. Furthermore, EVs provide more flexibility than other household loads in terms of energy storage. We evaluate our proposed algorithm using real data for electricity price, household consumption, and EV parameters. Adopting mutual information as a measure for information leakage, numerical results show that the proposed algorithm can reduce the customer's cost while maintaining the expected privacy level.
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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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".