PhD Work

The task of breaking down a household's electricity consumption into individual appliances is often referred to as non-intrusive appliance load monitoring (NIALM), or energy disaggregation. The field was introduced in the late 80s and early 90s, although it has recently gained momentum as a result of national smart meter deployments. Smart meters are household electricity meters which are deployed primarily for billing purposes, and therefore generally only measure the power demand of a household roughly once per second, although far less frequent readings are likely to be transmitted to the utility company. The focus of my PhD was the disaggregation of the power demand measured by such smart meters.

A household's power demand disaggregated into appliances

In order to perform such a disaggregation task, it is necessary to first build a mathematical model of a household. My work has adopted a hidden Markov model (HMM) based approach, in which appliances within a household are represented by HMMs. Each HMM contains a number of parameters which describe the behaviour of a particular appliance, such as its power demand or frequency of use. Since households contain many appliances, a household model can be constructed using a combination of HMMs (one for each appliance), a model known as the factorial hidden Markov model (FHMM). 

A hidden Markov model

There are many well studied algorithms for learning the parameters and also inferring unknown variables in HMMs and FHMMs. However, such approaches typically make assumptions that do not match the real-world scenario, such as requiring sub-metered data from each appliance in the home. This is obviously not a fair assumption, since it violates the 'non-intrusive' requirement for our system. My PhD has so far focused on the development of training and inference algorithms for such HMMs and FHMMs which enable NIALM technologies to be applied in the real world.

The interface to our NIALM system

I presented this work at AAAI-2012, and also presented a spotlight of this work at the First NILM Workshop, focusing on real-world training methods for NIALM systems. See my publications page for more details.

1st International Workshop on Non-Intrusive Load Monitoring