  With the help of smart metering valuable information of the appliance usage
can be retrieved. In detail, non-intrusive load monitoring (NILM), also called
load disaggregation, tries to identify appliances in the power draw of an
household. In this paper an unsupervised load disaggregation approach is
proposed that works without a priori knowledge about appliances. The proposed
algorithm works autonomously in real time. The number of used appliances and
the corresponding appliance models are learned in operation and are
progressively updated. The proposed algorithm is considering each useful and
suitable detected power state. The algorithm tries to detect power states
corresponding to on/off appliances as well as to multi-state appliances based
on active power measurements in 1s resolution. We evaluated the novel
introduced load disaggregation approach on real world data by testing the
possibility to disaggregate energy demand on appliance level.
