Testing Applicability of Virtual Stochastic Sensors for Non-Intrusive Appliance Load Monitoring

Abstract

Non-intrusive appliance load monitoring (NIALM) aims at reconstructing the electricity consumption of household appliances based only on the cumulative consumption data collected via a smart meter. Various approaches have been proposed to perform NIALM including various types of Hidden Markov Model (HMM) offspring. Most do not consider the explicit duration of an appliance activation. Virtual stochastic sensors (VSS) and their underlying Hidden non-Markovian Models (HnMM) can include explicit process durations. This paper tests whether VSS can solve the NIALM task, and analyzes the methods reconstruction accuracy on the publicly available SMART* data set. Models of the household appliances with different inherent states are automatically extracted from the appliance data. The combined model, including a subset of the appliances, is then used to disaggregate the cumulative energy consumption data. Experiments show a reconstruction accuracy of up to 90% with appropriate method parameters, showing that VSS can compete with existing NIALM approaches.