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.

The academic reality gap

Over the past three years I've read a lot of academic papers on the topic of energy disaggregation. However, the thing that frustrates me the most are some of the assumptions that are made. Here are a some of the most common:

Simulated houses

In the absence of actual household aggregate or individual appliance power data, some researchers test their disaggregation algorithms using synthetic data. While this might be useful for simulating a range of appliances and households, it's often hard to infer how the performance would map onto a real household.

Houses with < 10 appliances

Instrumenting a house with appliance sub-meters is intrusive and expensive, so often only a subset of appliances are monitored. Generally in this case, an artificial aggregate is then calculated by summing the power demand of each appliance, which is subsequently used as the input to the disaggregation algorithm. However, since the difficulty of disaggregation increases with the number of appliances, disaggregating this artificial aggregate is generally much easier than disaggregating the household's true aggregate.

Training data

Unfortunately for us working on disaggregation algorithms, appliances of the same type can vary quite a lot from house to house. As a result, a lot of research sidesteps this problem by requiring training data from the house in which disaggregation will be performed. Training data normally comes in the form of sub-metered appliance data or a training phase in which appliances are operated sequentially and manually labelled. However, collecting training data clearly will not scale at the same rate that smart meter deployments have done.

Known appliance types

Even worse than not knowing what model of appliance is in each house, is not knowing which appliance types are present in each house. This is the most reasonable of these four assumptions, since it is conceivable a household's occupants might be required to enter this information if they're interested to see disaggregated data. However, I can't imagine many non-enthusiasts would be willing to (accurately) list all the electric appliances in their home.

I think the first two problems have largely been solved by the release of many public data sets over the past few years. However, I think the last two problems are up to each individual researcher to ensure they're studying a realistic scenario. The field of energy disaggregation is growing at an incredible rate, and I think now is the time to tackle the complete problem rather than to only study its individual components.

Yetu and Verdigris added to list of disaggregation companies

I've recently come across Yetu and Verdigris, two companies aiming to make homes smarter. Yetu aim to wirelessly connect various components of smart homes together, such as electricity storage and micro-generation, as well as normal household appliances. Verdigris focus upon metering hardware and real-time appliance fault diagnosis. Both companies' products are built around core software disaggregation technology. I've also updated my ever-growing list of disaggregation companies with these two newcomers.

Would you replace your fridge for £82 per year?

A couple of weeks ago I gave a presentation to my research group giving an overview of my PhD research. I concluded with a slide showing some advice that could be provided to a real household from our most recent deployment. The slide showed that this household could save £82 per year by replacing their old inefficient fridge freezer with a new energy-efficient appliance. This was one of the least efficient fridge freezers we had come across, so I was pretty happy with the financial incentive for replacing it. However, the first question I received after the presentation was:

"The reward for replacing the appliance seems quite small, so how can you still motivate people to save energy? I know that wouldn't make me replace my fridge."

This really made me question the kind of feedback which disaggregation research aims to provide. My approach has always been to quantify the reward of some action, therefore empowering a household's occupants to make an informed decision, rather than making the decision for them. The reason for this being only the human can weigh up the inconvenience against the financial reward. I would guess that some people would replace their fridge given the same savings, and others would not. Personally, I'd replace my fridge for £82 per year, but would you?