Subtask Gated Networks for Non-Intrusive Load Monitoring

TL;DR

This paper introduces a subtask gated deep network for non-intrusive load monitoring that leverages appliance on/off states to improve energy disaggregation accuracy, surpassing previous methods on benchmark datasets.

Contribution

The paper proposes a novel subtask gated network architecture that combines regression and classification tasks for improved NILM performance, especially by modeling appliance on/off states.

Findings

Surpasses state-of-the-art performance on benchmark NILM datasets.

Effectively models appliance on/off states for better energy disaggregation.

Applicable to problems with inherent on/off states.

Abstract

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household's aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart's seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network's regression output with the subtask's classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.