Tuesday, 29 July 2014

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.

Friday, 25 July 2014

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!

Monday, 14 July 2014

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.