DP[formula omitted]-NILM: A distributed and privacy-preserving framework for non-intrusive load monitoring

Abstract

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household level into appliance-level consumption, can help analyze the electricity consumption behaviors of users and enable practical smart energy and smart grid applications. Recent studies have proposed many novel non-intrusive load monitoring frameworks based on federated deep learning. However, there is a lack of comprehensive research exploring the utility optimization schemes and the privacy-preserving schemes in different federated learning-based NILM application scenarios. In this study, a distributed and privacy-preserving non-intrusive load monitoring (DP2-NILM) framework was developed to make the first attempt to conduct federated learning-based NILM focusing on both utility optimization and privacy-preserving. Specifically, two alternative federated learning strategies are examined in the utility optimization schemes, i.e., the FedAvg and the FedProx. Moreover, different levels of privacy guarantees, i.e., the local differential privacy federated learning and the global differential privacy federated learning are provided in the DP2-NILM. Extensive comparison experiments are conducted on three real-world datasets to evaluate the proposed framework.