Disaggregation in the UK

I was recently asked about smart meter legislation in UK, and its direction with respect to energy disaggregation. Here was my response:

As far as I'm aware, UK smart meters are not required to provide appliance specific electricity breakdowns. Instead, they're being installed primarily for automatic billing purposes, but also to provide total household consumption information and potentially real-time pricing data via in home displays. Some general information about UK smart meters is available from the UK Government, and the latest technical specification for smart meters is the Smart Metering Equipment Technical Specification v2.

In the UK, most consumers are signed up to electricity contracts with one of the big 6 energy suppliers. However, British Gas, the largest of these suppliers, recently started running a TV advert in which their smart meters were shown to break down their households' electricity usage into heating and lighting. In my opinion, this shows that although appliance specific breakdowns are not required by government directives, energy suppliers are keen to provide such services in order to incentivise consumers. I think breaking down electricity usage into heating and lighting is only the beginning of what electricity disaggregation can offer to consumers, and we're likely to see some interesting competition in this domain between some of the major players in the UK energy sector.

Incorporating general appliance knowledge into disaggregation algorithms

I've recently been thinking a lot about how to incorporate prior knowledge into disaggregation training algorithms. By prior knowledge, I mean general information about how appliance types operate, i.e. what makes a fridge different from a washing machine. However, this prior knowledge should not be specific to a single household, and should therefore generalise to previously unseen households. Clearly, such prior knowledge is required if a NIALM system is to operate without manual intervention. To date, I have seen two categories of approaches which incorporate prior information into the learning process, which I have described below.

Sequential learning and labelling

This approach first aims to identify the characteristics of the appliances in a household without any prior knowledge. This produces a list of appliances (e.g. appliance 1, appliance 2) with their corresponding behaviour. Next, this category of approaches use a second step to assign labels to the learned appliances (e.g. appliance 1 = fridge, appliance 2 = washing machine). A diagram of this approach is given below:

This approach has been adopted in some recent state-of-the-art disaggregation papers. Both Kim et al. (2011) and Kolter et al. (2012) both use an unsupervised learning approach to the first step, while the manual labelling of learned appliance models is required by the second step.

Simultaneous learning and labelling

This approach aims to use both aggregate data and prior knowledge in order to simultaneously learn appliance models, as shown below:

This seems to be a more principled approach, in that prior knowledge is not ignored when the learning algorithm identifies distinct appliances within the aggregate load. We demonstrated such an approach in a recent paper (Parson et al., 2012). Furthermore, this type of approach lends itself well to Bayesian learning, whereby the learned appliances models constitute a weighted combination of information extracted from aggregate data and the general appliance models. Such an approach is detailed in Johnson and Willsky (2013).

In summary, I believe it is essential to incorporate such general appliance knowledge into the learning algorithms of energy disaggregation systems to allow them to scale to large numbers of previously unseen households. Only in the past few years has published work come close to Hart's vision of a Manual Setup NIALM (Hart, 1992), but the problem is still far from solved.

Outstanding student paper award at IJCAI-13

A while ago, I wrote a blog post about our paper which aims to detect the New Forest Cicada from smartphone audio recordings. Davide Zilli recently presented this work in Beijing, and I'm very happy to announce that paper was awarded the outstanding student paper award. Although we're yet to rediscover the cicada native to the UK, the app has collected thousands of audio recordings worldwide and located similar species of cicada in nearby countries. There are still a few more days left of the cicada season in the UK, so check out the New Forest Cicada Project website for more details if you're thinking of heading to the New Forest.

The New Forest Cicada

IJCAI-13 tutorial on Topics in Computational Sustainability

Carla Gomes and Zico Kolter very recently organised a computational sustainability tutorial at IJCAI-13. The tutorial was centred around three sustainability problems: energy generation and demand forecasting, energy disaggregation, and control of power networks. I found it really interesting to see these AI and sustainability problems all grouped together, as a question I've discussed with Jack before is: "what problem problem should I work on in order to have the greatest impact on sustainability?" Furthermore, Zico has made some of the data and MATLAB code used for some of the demonstrations available online, which would be a great starting point for a dissertation project.