Post NILM 2014 @ London

On Wednesday 3 September we held the NILM 2014 @ London meet up at Imperial College London. The aim of the meet up was to provide a local forum for European energy disaggregation researchers to get to know each other and discuss their work. The agenda included introductions of the attendees, an overview of NILM events to date, a presentation of energy disaggregation at DNO level, an overview of the NILMTK project, as well as many discussion around data collection and evaluation. The meet up was attended by representatives from AlertMe, British Gas, Green Running, Greeniant, Navetas, ONZO and Wattgo, as well as academics from Imperial College London, University of Klagenfurt and University of Southampton. Slides from the presentations are now online via the meet up website, while photos from the day are available via Jack Kelly's flickr album.

It became clear from the meet up that there's real enthusiasm for a regular NILM event hosted in Europe. Currently, we're planning to host the next event in London around February-March 2015, so please get in touch if you'd like to attend.

Special thanks to Peter Davies of Green Running for sponsoring the event and Jack Kelly of Imperial College London for the local organisation.

NILM 2014 @ London update

With 1 week to go until the meet up, I wanted to give an update on the recent developments. We currently have attendees signed up from 7 companies and 4 academic institutions, and will continue to accept registrations up until the day before the event. The location for the meet up has also been confirmed as a lecture room at Imperial College London. Finally, we'll likely hold a NILMTK hackday the following day (Thursday 4th September 2014) for anyone who is interested.

I look forward to seeing you there!

50 day access to my Artificial Intelligence article

The published version of my recently accepted paper in Artificial Intelligence is now available online for free for 50 days. However, after the 4 October 2014, access to the published version will require a subscription to the journal, although a pre-print of the article will always be available via the University of Southampton ePrints repository.

NILM 2014 @ London, UK

Peter Davies and I would like to invite you to a meetup on the topic of non-intrusive load monitoring, to be hosted in central London on 3 September 2014. The purpose of the workshop is to provide a forum to share common research goals and identify common themes for collaboration. The workshop will be free to attend, although registration via the meetup web page is required. Further details can be found at:

http://www.oliverparson.co.uk/nilm-2014-london

We look forward to seeing you there!

Paper accepted to Journal of Artificial Intelligence

Last week we got the notification that our paper had been accepted to appear in the Journal of Artificial Intelligence. The paper describes a method for building generalisable appliance models from existing data sets, and also a method which tunes such models for previously unseen households using only aggregate smart meter data. This material also appeared in chapters 4 and 5 of my thesis.

The final reference for the article is:

Oliver Parson, Siddhartha Ghosh, Mark Weal, Alex Rogers. (2014). An Unsupervised Training Method for Non-intrusive Appliance Load Monitoring. In: Artificial Intelligence, 217, 1–19.

and the abstract is:

Non-intrusive appliance load monitoring is the process of disaggregating a household's total electricity consumption into its contributing appliances. In this paper we propose an unsupervised training method for non-intrusive monitoring which, unlike existing supervised approaches, does not require training data to be collected by sub-metering individual appliances, nor does it require appliances to be manually labelled for the households in which disaggregation is performed. Instead, we propose an approach which combines a one-off supervised learning process over existing labelled appliance data sets, with an unsupervised learning method over unlabelled household aggregate data. First, we propose an approach which uses the Tracebase data set to build probabilistic appliance models which generalise to previously unseen households, which we empirically evaluate through cross validation. Second, we use the Reference Energy Disaggregation Data set to evaluate the accuracy with which these general models can be tuned to the appliances within a specific household using only aggregate data. Our empirical evaluation demonstrates that general appliance models can be constructed using data from only a small number of appliances (typically 3-6 appliances), and furthermore that 28-99% of the remaining behaviour which is specific to a single household can be learned using only aggregate data from existing smart meters.

PhD Graduation

This week I finally graduated from my PhD, nearly four years after I started my work on energy disaggregation (and this blog) back in October 2010. It was cool to finally get closure on the whole process, and celebrate our achievements along with four other PhD students from our lab. Fortunately, I'm still working in the field of non-intrusive load monitoring, so this blog will remain active for at least the next six months. In case you missed it, here's a link to my thesis, which I already feel like was finished a long time ago!

Workshop on Human Centric Energy Management

Last week I attended a workshop on human-centric energy management organised by Dominik Egarter, Wilfried Elmenreich and Martin Krch in Klagenfurt, Austria. The schedule featured a series of keynote talks, discussions, and a trip to Kraftwerk Forstsee.

I presented some work from my group on disaggregating a home's fridge-freezer energy consumption from smart meter data, and also some work on providing home heating feedback via the Joulo project. My full presentation slides are available via my website.

The workshop also included talks from Wilfried Elmenreich on work from their group aiming to bring the smart grid into people's homes, and also Andreas Reinhardt on the opportunities for novel services based on appliance-level power consumption monitoring.

I really want to thank the organisers for inviting me to attend such a great workshop. I'm particularly excited about the potential for collaborations within NILMTK, and hope it leads to some interesting joint projects.