Wednesday, 29 May 2013

Data set released by EDF Energy

I've only just come across this data set, despite it being released almost a year ago! I've also updated my post of public data sets.

EDF Energy released a data set in 2012 containing energy measurements made at a single household in France for a duration of 4 years. Average measurements are available at 1 minute resolution of the household aggregate active power, reactive power, voltage and current, as well as the active power of 3 sub-metered circuits. Although each circuit contains a few appliances, this is the largest data set in terms of duration of measurement. The complete data set is openly available from the UCI Machine Learning Repository.

Saturday, 25 May 2013

The pros and cons of using HMMs to model appliances

In the last few years, hidden Markov models (HMMs) have become a very popular mathematical representation for appliances (Zia et al. 2010, Kim et al. 2011, Kolter et al 2012, Parson et al 2012). As a result, I'm often asked whether I think HMMs are the future of disaggregation. However, I'm yet to find an objective analysis of the advantages and disadvantages of such approaches, which is why I've done my best to list them here:

• The HMM is a well studied probabilistic graphic model, for which algorithms are known for exact and approximate learning and inference
• HMMs are able to represent the variance of appliances' power demands through probability distributions
• HMMs capture the dependencies between consecutive measurements, as defined by Hart as the switch continuity principle

• HMMs represent the behaviour of an appliance using a finite number of static distributions, and therefore fail to represent appliances with a continuously varying power demand
• Due to their Markovian nature, they do not take into account the sequence of states leading into any given state
• Again, due to their Markovian nature, the time spent in a given state is not captured explicitly. However, the hidden semi-Markov model does capture such behaviour
• Features other than the observed power demand are not captured (e.g. time of day). However, the input-output HMM allow such such state durations to be modelled
• Any dependency between appliances cannot be represented. However, the conditional-HMM can capture such dependencies

In summary, the basic HMM provides a useful model for many appliances. However, the appliances it can represent are limited by the intrinsic structure of the model. Many extensions exist that increase the representational power of the HMM, although the additional parameters required often complicate the learning and inference tasks.

Wednesday, 22 May 2013

AAAI 2012 Code Release

A while ago, I wrote a post stating that I was planning to release my NIALM code at the end of my PhD. I also mentioned in the post that I'd been happily giving out an archive of my code upon request. Since then, I've had far more requests than I'd expected, as well as quite a few technical questions regarding how to run it. As a result, I've decided to make my code from my AAAI 2012 paper available via my github for anyone to clone or contribute to.

The reason why I hadn't previously uploaded my code is that I simply do not have time to provide documentation or tutorials for using my code. Therefore, my code is provided "as is", so apologies in advance if you don't find it easy to use!

Update 15.09.2015: updated link to point to github

Friday, 3 May 2013

Trip to the Minnesota, New York and North Carolina

I'll be visiting the US states of Minnesota, New York and North Carolina in the coming weeks, so please give me a shout if you're nearby and would like to talk disaggregation!