A Generalizable Method for Practical Non-Intrusive Load Monitoring via Metric-Based Meta-Learning

Abstract

Non-Intrusive Load Monitoring (NILM) has become a research boom due to its energy-saving benefits as the smart grid develops and the greenhouse effect intensifies. However, existing NILM models may perform poorly in the target domains collected from different sources, or those containing unseen appliance types, blocking the rollout of NILM. In contrast to the methods that up-date the trained model on the unseen data to handle domain shift or heterogeneous label space, this paper proposes a novel general-izable NILM method based on metric-based meta-learning and su-pervised pre-training. It beforehand learns the cross-domain meta-knowledge through meta-training to eliminate the need for parameter updates in the target domain. Moreover, this method is specifically designed for practical deployments at the edge where both labeled instances and computation resources are limited. The proposed method transforms the appliance current into a metric vector via lightweight 1D convolution and then uses the relation module to propagate the labels via similarity. The experimental results on three public datasets and the proposed dataset demon-strate that the proposed method can achieve high generalization performance in different datasets, users houses, appliance brands, and appliance types.