April 2017 update of UK-DALE

I've just updated my UK Domestic Appliance-Level Electricity (UK-DALE) dataset. House 1 now has 4.3 years of data!

Free, 2nd hand, data collection hardware

I'm giving away some of the data collection hardware I used for UK-DALE. Details here.

PhD thesis is all done!

My PhD examiners were happy with the minor corrections I made to my PhD thesis! So I'm all done with my PhD thesis :)

How to mitigate climate change using computer science?

The question of how best to mitigate climate change using computer science is something I ponder a lot. So I've started keeping a list of ideas on GitHub: Climate Change Mitigation for Hackers. Please do suggest additions or edits!

Not currently working on the energy disaggregation competition

Towards the end of last year, I was lucky enough to have a short postdoc paid for by EDF Energy. The main focus of the postdoc was on looking at ways to design a competition to compare the performance of different disaggregation algorithms. This postdoc finished in January 2017 so I am not currently working on the disaggregation competition (although I strongly believe that finding a good way to compare NILM algorithms is one of the most important unsolved problems in NILM).

Very briefly: the main challenge in designing a NILM competition is getting enough clean, private testing data. It turns out that the performance of NILM algorithms can be quite inconsistent across houses: an algorithm might work well on some houses; but on other houses that same algorithm might work badly. Also, one of the promising uses of NILM is to identify "extreme" energy behaviour (such as leaving your electric oven on constantly just in case you fancy doing some baking). Identifying "extreme" behaviour is useful because users can save large sums of money with a single, simple change in behaviour. But - by definition - "extreme" behaviour is rare. Hence we need a large testing dataset (maybe 100 houses) to be confident that we're accurately capturing the performance of each algorithm; and that each algorithm can recognise "extreme" energy behaviour. Recording this quantity of real data would be very expensive and time consuming. Hence we could consider building a high-quality simulator to generate realistic data. But this raises a whole host of additional challenges!

Making a "haunted staircase" for Halloween using a Touch Board & Ableton Live

P1040124View on Flickr

For Halloween 2016, we made a "haunted staircase". Spooky sounds were triggered as unsuspecting trick-or-treaters walked up the stairs outside our house. This project won the Bare Conductive Halloween competition! This blog post describes how we made our staircase...

(the photo above was taken by my wife, Ginnie)

Here's a video of the final result, as demonstrated by my five year-old daughter, Olive:

Simulating disaggregated electricity data

To do rigorous NILM research, we need lots of high-quality disaggregated electricity data. This is especially true if we want to run a good NILM competition.

There are now 20 public datasets listed on the NILM wiki. But all real data suffers from problems which make it problematic for use in a NILM competition. These problems include:

Survey launched: Please help us to design a competition for energy disaggregation algorithms!

We are working on a competition for energy disaggregation algorithms. Please help us to design this competition by filling in this survey!

A competition for energy disaggregation algorithms

Now that I've (finally!) submitted my PhD thesis, I can focus on designing and implementing a competition for energy disaggregation algorithms. EDF Energy have kindly given me post-doc funding from now until the end of December 2016 to work on the NILM competition.

The broad plan is to first consult with the NILM community and create a specification for the NILM competition which works for everyone. Then I plan to implement a web application which can run the NILM competition.

Right now, I'm writing a survey on the design of a competition for energy disaggregation algorithms. The aim of the survey is to systematically collect feedback about the design of the competition. I plan to launch the survey soon. Prior to the launch, I'm really eager to hear feedback on the survey itself. For example: is the survey missing any vital questions? Do some questions not provide sufficient options? Do some questions not make sense?!

Please note that, prior to the launch of the survey, my aim is to get feedback on the design of the survey itself. So please don't actually submit any answers yet! Feel free to select options and click "next" but just please don't click "submit" at the end of the survey. I'll write another blog post when the survey is ready to accept answers.

It's probably best to provide feedback about the survey in public on the relevant thread on the Energy Disaggregation Google Group. If you want your feedback to be private then, by all means, email me directly at!

And please do get in touch if you have feedback on any aspect of the proposed NILM competition.


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