student project

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

The wonderful MSc students I worked with last year and their award-winning projects ;)

This spring and summer I had the opportunity to work with some exceptional MSc students at Imperial and I just wanted to write a summary of their achievements.

Visualisation of machine learning algorithms: computer science group project proposal

Here's another computer science group project that I have submitted for consideration by students this coming year. As always, comments are very welcome!


Algorithms used in machine learning can often feel quite complex when you first come across them. As you gain experience with the algorithm you begin to be able to visualise each step and then realise that the algorithm is actually quite intuitive. Wouldn't it have been far easier to learn the algorithm if you had seen a good visualisation of the algorithm to begin with!

For example, here's a visualisation of selection sort (taken from WikiPedia):

The aim of this project is to produce interactive, animated visualisations of a set of machine learning algorithms. (Don't worry if you don't know any machine learning algorithms yet; this project would be a good opportunity to gain intimate knowledge of a few algorithms).

Below are some suggestions to get your ideas flowing.

Open repository for appliance power signatures: computer science group project proposal

Here's another draft proposal for a computer science group project that I'm thinking of submitting...


Introduction

As energy costs increase, there is increasing pressure to use energy as efficiently as possible. The first step towards reducing energy consumption is often to measure your existing consumption. There is good evidence that people are best able to manage their energy consumption if they are given an itemised energy bill describing appliance-by-appliance energy consumption information (e.g. "your fridge cost you £50 this month and your TV cost you £20").

Automatically estimating an itemised energy bill by "disaggregating" a whole-house smart meter signal is an active area of research. One approach to this problem requires that the disaggregation system be trained on existing appliance "signatures" (recordings of the power consumption of an individual appliance). A web service which allows for appliance signatures to be programmatically retrieved and submitted would be very useful both to enable automatic disaggregation systems and also to encourage research into disaggregation.

The Challenge

The aim of this project would be to build a web application to allow appliance signatures to be submitted, categorised, analysed and retrieved.

UK energy infrastructure simulation game: computer science group project proposal

Here's a draft proposal for a computer science group project that I'm thinking of submitting...


Could we power the entire of the UK electricity grid on renewables alone? What about a mixture of nuclear and renewables? How much would this scenario cost to build? Or what about going to the other extreme and switching our power generation to 100% coal? What implications would that have?

Discussions about energy are becoming quite common in the media. Questions like those listed above are frequently asked but it's hard to find good answers. One of the big problems in communicating energy issues to the wider public is that people have little sense of the scales involved.

The aim of this project will be to build a kind of "SimCity" game to allow members of the public to explore different energy scenarios for the UK (or for the whole world if you're feeling very ambitious!). Users would be allowed to input any scenario and the game would simulate the consequences. Points are lost for triggering power blackouts or excessive environmental damage.

"Smart room-by-room heating control for homes"

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

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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