  With the roll-out of smart meters the importance of effective non-intrusive
load monitoring (NILM) techniques has risen rapidly. NILM estimates the power
consumption of individual devices given their aggregate consumption. In this
way, the combined consumption must only be monitored at a single, central point
in the household, providing various advantages such as reduced cost for
metering equipment. In this paper we discuss the fundamental building-blocks of
NILM, first giving a taxonomy of appliance models and device signatures and
then explaining common supervised and unsupervised learning methods.
Furthermore, we outline a fundamental algorithm that tackles the task of NILM.
Subsequently, this paper reviews recent research that has brought novel insight
to the field and more effective techniques. Finally, we formulate future
challenges in the domain of NILM and smart meters.
