  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.
