  Non-intrusive load monitoring (NILM) has been extensively researched over the
last decade. The objective of NILM is to identify the power consumption of
individual appliances and to detect when particular devices are on or off from
measuring the power consumption of an entire house. This information allows
households to receive customized advice on how to better manage their
electrical consumption. In this paper, we present an alternative NILM method
that breaks down the aggregated power signal into categories of appliances. The
ultimate goal is to use this approach for demand-side management to estimate
potential flexibility within the electricity consumption of households. Our
method is implemented as an algorithm combining NILM and load profile
simulation. This algorithm, based on a Markov model, allocates an activity
chain to each inhabitant of the household, deduces from the whole-house power
measurement and statistical data the appliance usage, generate the power
profile accordingly and finally returns the share of energy consumed by each
appliance category over time. To analyze its performance, the algorithm was
benchmarked against several state-of-the-art NILM algorithms and tested on
three public datasets. The proposed algorithm is unsupervised; hence it does
not require any labeled data, which are expensive to acquire. Although better
performance is shown for the supervised algorithms, our proposed unsupervised
algorithm achieves a similar range of uncertainty while saving on the cost of
acquiring labeled data. Additionally, our method requires lower computational
power compared to most of the tested NILM algorithms. It was designed for
low-sampling-rate power measurement (every 15 min), which corresponds to the
frequency range of most common smart meters.
