  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.
