Wednesday, 31 July 2013

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.

Tuesday, 23 July 2013

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.

Monday, 22 July 2013

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?

Friday, 12 July 2013

Belkin Energy Disaggregation Competition

I've just come across an energy disaggregation competition set up by Belkin on the Kaggle platform. The competition focuses on the disaggregation of high frequency data, from which Belkin provide the following features:

  • Spectrogram of high frequency noise
  • Fundamental and first 5 current harmonics on each phase
  • Fundamental and first 5 voltage harmonics on each phase

The competition supplies this data in two sets:

  1. Training set - includes both aggregate features and appliance ground truth
  2. Test set - includes only aggregate features

This idea is for participants to train their disaggregation algorithms on the training set, and upload the result of their disaggregation algorithms on the test set. Participants will then receive a score reflecting the accuracy of their algorithm's output. The deadline for the competition is 30 October 2013, and the top prize is $14,000, so get busy disaggregating!

Saturday, 6 July 2013

Crowdsourcing gas leaks with iSmellGas

I've recently become interested in crowdsourcing platforms, and how such methodologies can be applied to sustainability issues. One thing I noticed was how often I smelt the smell of gas while walking around cities. I spoke to my friends who also seemed to have smelt gas leaks, although no one had got around to reporting them. This inspired myself and a friend to create iSmellGas, a platform for crowdsourcing the location and strength of gas leaks. The idea is that gas leaks can be reported via the Android mobile app, and collected online via our web app.

Please take a look at our website and share it in any way you like, and if you have an Android phone, download our app and start reporting!

Friday, 5 July 2013

Global map of energy disaggregation research

Keeping track of who is working within a research field is a tough task, especially when it's expanding at the rate of the energy disaggregation domain. A while ago, I created a map of the hits to my blog grouped by individual countries. However, I can't imagine the map was particularly useful, given that it wasn't possible to drill down beyond the country level. For this reason, I decided to set up a Google map to allow anyone to add their own institution or company to the list. The map is editable by anyone, so please feel free to add whatever information about your research you wish.

View Energy disaggregation research institutions in a larger map