Learning Task-Aware Energy Disaggregation: a Federated Approach

TL;DR

This paper introduces a federated, task-aware learning approach for energy disaggregation in residential settings, addressing data heterogeneity and privacy concerns while improving appliance-level consumption inference.

Contribution

It proposes a novel decentralized, task-adaptive learning scheme combining meta learning and federated learning for NILM, enabling effective model training without centralized data collection.

Findings

Efficient appliance-level consumption inference across diverse homes.

Robust performance on benchmark datasets.

Addresses privacy and heterogeneity challenges in NILM.

Abstract

We consider the problem of learning the energy disaggregation signals for residential load data. Such task is referred as non-intrusive load monitoring (NILM), and in order to find individual devices' power consumption profiles based on aggregated meter measurements, a machine learning model is usually trained based on large amount of training data coming from a number of residential homes. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data, while load data coming from different regions or electricity users may exhibit heterogeneous usage patterns. Both practical concerns make training a single, centralized NILM model challenging. In this paper, we propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively. Simulation results on benchmark dataset validate proposed algorithm's performance on efficiently inferring appliance-level consumption for a variety of homes and appliances.