  Non-intrusive load monitoring (NILM) aims at separating a whole-home energy
signal into its appliance components. Such method can be harnessed to provide
various services to better manage and control energy consumption (optimal
planning and saving). NILM has been traditionally approached from signal
processing and electrical engineering perspectives. Recently, machine learning
has started to play an important role in NILM. While most work has focused on
supervised algorithms, unsupervised approaches can be more interesting and of
practical use in real case scenarios. Specifically, they do not require
labelled training data to be acquired from individual appliances and the
algorithm can be deployed to operate on the measured aggregate data directly.
In this paper, we propose a fully unsupervised NILM framework based on Bayesian
hierarchical mixture models. In particular, we develop a new method based on
Gaussian Latent Dirichlet Allocation (GLDA) in order to extract global
components that summarise the energy signal. These components provide a
representation of the consumption patterns. Designed to cope with big data, our
algorithm, unlike existing NILM ones, does not focus on appliance recognition.
To handle this massive data, GLDA works online. Another novelty of this work
compared to the existing NILM is that the data involves different utilities
(e.g, electricity, water and gas) as well as some sensors measurements.
Finally, we propose different evaluation methods to analyse the results which
show that our algorithm finds useful patterns.
