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

TL;DR

This paper introduces a deep reinforcement learning algorithm for residential load-shaping that enhances user privacy by generating artificial load signatures, outperforming existing methods while balancing privacy and cost.

Contribution

It presents a novel PLS-DQN algorithm that proactively conceals energy usage patterns, advancing privacy protection in smart meter systems.

Findings

Effectively conceals real energy usage patterns from NILM adversaries

Outperforms state-of-the-art load-shaping methods in privacy protection

Maintains cost efficiency while enhancing privacy

Abstract

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.