Thursday, 29 March 2012

Paper accepted at AAAI on NIALM training

I recently received notification that my paper titled Non-intrusive Load Monitoring using Prior Models of General Appliance Types has been accepted at AAAI-2012. The paper will appear in the Computational Sustainability for AI track, for which I will give an oral and poster presentation at the conference. The abstract for the paper is below:

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 approach by which individual appliances can be iteratively separated from an aggregate load. Unlike existing approaches, our approach does not require training data to be collected by sub-metering individual appliances, nor does it assume complete knowledge of the appliances present in the household. Instead, we propose an approach in which prior models of general appliance types are tuned to specific appliance instances using only signatures extracted from the aggregate load. The tuned appliance models are then used to estimate each appliance's load, which is subsequently subtracted from the aggregate load. This process is applied iteratively until all appliances for which prior behaviour models are known have been disaggregated. We evaluate the accuracy of our approach using the REDD data set, and show the disaggregation performance when using our training approach is comparable to when sub-metered training data is used. We also present a deployment of our system as a live application and demonstrate the potential for personalised energy saving feedback.

Full details can be found on my publications page.


  1. oh, that's swell!! congratulations!

  2. Congratulations!

    I have enjoyed reading your page. I am working rather with 'intrusive' demand management (i.e. using newtorking).

    I am looking for multi appliance, multi household, extended period 'real' demand data sets to test my energy management system. Do you knwo if such data sets are available or if perhaps there are sufficient NIALM training data sets (these are at point of appliance right?) available?

    Many thanks.

    Andrew Hardy (

  3. Hi Andrew,

    Thanks! It's always good to hear from people working in a similar field.

    With regard to data sets, it sounds like the REDD data set offers what you're looking for. I'd actually already written half a blog post about data sets, so thanks for reminding me to finish it! The post contains information about the REDD data set and one other public data set I've come across. Check it out here: