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.

提出了一种基于深度强化学习的负载整形算法 (PLS-DQN)，通过主动创建人工负载签名来误导潜在攻击者，旨在保护用户隐私，同时保持成本效益。评估结果表明，该方法不仅有效隐藏真实能源使用模式，而且在提高用户隐私的同时也超越了最先进的方法。