Smart Meter Privacy: Exploiting the Potential of Household Energy Storage Units
Bibliographic record
Abstract
The Internet of Things (IoT) extends network connectivity and computing capability to physical devices. However, data from IoT devices may increase the risk of privacy violations. In this paper, we consider smart meters as a prominent early instance of the IoT, and we investigate their privacy protection solutions at customer premises. In particular, we design a load hiding approach that obscures household consumption with the help of energy storage units. For this purpose, we leverage the opportunistic use of existing household energy storage units to render load hiding less costly. We propose combining the use of electric vehicles (EVs) and heating, ventilating, and air conditioning (HVAC) systems to reduce or eliminate the reliance on local rechargeable batteries for load hiding. To this end, we formulate a Markov decision process to account for the stochastic nature of customer demand and use a Q-learning algorithm to adapt the control policies for the energy storage units. We also provide an idealized benchmark system by formulating a deterministic optimization problem and deriving its equivalent convex form. We evaluate the performance of our approach for different combinations of storage units and with different benchmark methods. Our results show that the opportunistic joint use of EV and HVAC units can reduce the need of dedicated large-capacity or fast-charging-cycle batteries for load hiding.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".