CALM: Contactless Accurate Load Monitoring via Modality Distillation

Abstract

The rapid proliferation of Smart Grids calls for a more in-depth understanding of user energy consumption behaviors, based on large data volumes collected by various sources of sensors such as voltmeter and ammeter. Non-Intrusive Load Monitoring (NILM) is a single sensor solution, which can effectively disaggregate individual appliance states from measurements only at the interface to the power source, albeit at the cost of requiring circuit modifications thus introducing suspension of services and potential safety hazards. To overcome the undesirable attribute of NILM and achieve a safe yet highly accurate solution, we devise a contactless sensing system based on inductive current measurements that can conduct load disaggregation without tampering with the power system. Despite using single modality, i.e., the inductive current, our scheme attains state-of-the-art accuracy in existing multi-modality datasets by leveraging modality distillation technique to handle arbitrary input structure. Our main contributions enlist: (1) devising and deploying the first, to the best of our knowledge, purely contactless non-intrusive load disaggregation system; (2) the design of an oracle-apprentice network structure to leverage multi-modality input for training, while operating with single modality; (3) a high estimation accuracy of 95.44% and 96.21%, respectively, is attested on two public datasets, which proves the efficiency of our method.