MSc

MSc project proposal: An online competition for comparing energy disaggregation algorithms

A sizeable challenge in the energy disaggregation community is that of comparing NILM algorithms from different researchers. In other words, if we have two papers, and one paper reports an accuracy of 80%, and another reports an accuracy of 85% then we cannot infer that the second paper is better because the authors used different datasets, different pre-processing etc. Hence we are working on a project proposal for the consideration of Imperial Computer Science MSc students. If a group of students selects the project then they'll work on it for the duration of next term. Here's the full, draft project specification. Comments most welcome!

Make machine learning easy enough for kids to use in their creations (MSc group project proposal)

The aim of this project is to make sophisticated machine learning tools so easy to use that kids (and adults with little knowledge of computer science) can use machine learning algorithms in their creations. The approach is to wrap machine learning tools as simple ‘building blocks’ which can be bolted together with existing rapid prototyping tools to allow users to quickly throw together sophisticated inventions. e.g. Lego robots which can classify objects using a video camera, or can understand natural speech, or learn some task through trial and error (e.g. balancing on one leg). Full (draft) spec here

Project proposal for an MSc individual project on disaggregation

Imperial MSc Computing Science students do a 3-month individual project over summer. Below is a proposal I and my Ph.D. supervisor have just submitted. Of course, there are no guarantees that any students will be interested...

"Inferring appliance-by-appliance energy consumption from whole-house electricity meter readings"

By the end of the decade, every house in the UK will have a "smart meter" installed. Each smart meter will record the electricity consumption for the whole house once every ten seconds.

There is good evidence that people find appliance-by-appliance information to be considerably more useful than whole-house aggregate information when making decisions about saving energy. Hence it would be very useful to be able to disaggregate whole-house electricity meter signals into appliance-by-appliance information.

The aim of this project is to implement a disaggregation algorithm and evaluate its performance against real data. The design of the disaggregation algorithm can be your own invention or an algorithm already described in the literature. There are many approaches to this problem and you will be free to choose an approach.

We have a dataset recorded from multiple houses over several months (for each house we recorded the whole-house current and voltage waveforms at 8kHz as well as the "ground truth" of how much power individual appliances are actually using). We also have funding to install meters in your own home, if you wish.

Further reading:

For the "classic" paper on this topic, see:

G. W. Hart, ‘Nonintrusive appliance load monitoring’, Proceedings of the IEEE, vol. 80, no. 12, pp. 1870–1891, Dec. 1992. DOI:10.1109/5.192069

For a recent review of the literature, see:

K. C. Armel, A. Gupta, G. Shrimali, and A. Albert, ‘Is disaggregation the holy grail of energy efficiency? The case of electricity’, Energy Policy, vol. 52, pp. 213 – 234, 2013. DOI:10.1016/j.enpol.2012.08.062

My MSc project on disaggregation is on the Imperial website

During the academic year 2010-2011, I did a computer science MSc at Imperial (which I thoroughly enjoyed). During the last 3 months of the course, each student does an "individual project". Mine was on "Disaggregating Smart Meter Readings using Device Signatures" and the PDF is now available on the Imperial website (note that my birth name is "Daniel" although I've had the nickname "Jack" since I was 11!)

This MSc project formed the basis for my PhD (I'm doing my PhD with the same excellent supervisor with whome I did my MSc project). 4 months into my PhD, I now recognise that my MSc project was pretty naive but it was lots of fun!

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