# Proactive Load-Shaping Strategies with Privacy-Cost Trade-offs in Residential Households based on Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2405.18888 · HTML: https://mlsanity.com/paper/2405.18888/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 4 of 4)

- [Neural NILM: Deep Neural Networks Applied to Energy Disaggregation](https://mlsanity.com/paper/1507.06594/) (2015)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [How You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning](https://mlsanity.com/paper/1904.11082/) (2019)
- [Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning](https://mlsanity.com/paper/2003.04946/) (2020)

## Built on by (most cited first, 1 of 1)

- [Learning Defensive Policies against Diverse Inference Attacks for Smart Meter Privacy](https://mlsanity.com/paper/2609.25484/) (2026)

## Abstract (arXiv)

Smart meters play a crucial role in enhancing energy management and efficiency, but they raise significant privacy concerns by potentially revealing detailed user behaviors through energy consumption patterns. Recent scholarly efforts have focused on developing battery-aided load-shaping techniques to protect user privacy while balancing costs. This paper proposes a novel deep reinforcement learning-based load-shaping algorithm (PLS-DQN) designed to protect user privacy by proactively creating artificial load signatures that mislead potential attackers. We evaluate our proposed algorithm against a non-intrusive load monitoring (NILM) adversary. The results demonstrate that our approach not only effectively conceals real energy usage patterns but also outperforms state-of-the-art methods in enhancing user privacy while maintaining cost efficiency.