EEme release disaggregation accuracy calculated by Pecan Street

I recently came across EEme, a spin-out from Carnegie Mellon University, who apply energy disaggregation to 15-minute smart meter data to provide demand side management analytics. EEme are particularly interesting, since they’re the first company (to the best of my knowledge) to go completely public regarding the accuracy of their product, as calculated by a third party. EEme used Pecan Street’s 3rd party evaluation tool, which provided EEme with 15-minute aggregate smart meter data and weather data from hundreds of homes, and required EEme to return monthly totals for four of the largest energy consuming appliances. Since Pecan Street also measured the appliance-level energy consumption as part of their deployment, they are able to calculate the exact level of accuracy of EEme’s disaggregation. The full 7 page report is available for request from EEme’s website. Although EEme focus on lower resolution data than many other energy disaggregation companies, I’m interested to see whether this report sparks the beginning of a more public competition between energy disaggregation companies to release accuracy statistics as confirmed by a third party.

Overview of the NILM field

This post aims to use trends in recent publications to provide an overview of the field of non-intrusive load monitoring / energy disaggregation.

Recent Growth

Researchers often refer to a recent explosion in the number of NILM publications. The graph below shows the number of papers published per year, from which the upward trend since 2010 is clearly visible. This renewed interest is likely due to recent country-wide roll outs of smart meters.

Significant Publications

In such a rapidly growing field, it's often hard to understand which publications are the most significant. The graph below shows the number of citations of the most cited papers. Unsurprisingly, Hart's 1992 seminal paper is the most cited by far, with a number of other papers from the 90s also appearing high up the list.

Since older papers have had more time to accumulate citations, it's also interesting to look at citations per year to get a better idea of recent trends in the field, as shown by the graph below. Unlike before, there is no stand-out paper, with recent review papers and data set papers receiving the greatest citation velocity. Besides these papers, a number of the remaining highly cited papers propose techniques based upon principled machine learning models.

Publishers

I am often asked which are the most popular venues for NILM research. The graph below shows the most popular publishers, with ieeexplore clearly publishing the most papers in this field. Unfortunately, I couldn't get hold of high quality data for conferences/journals, which I'm sure would have been useful.

Keywords

Finally, it's also interesting to analyse common keywords in existing publications. The graph below shows the most commonly occurring words in paper titles, with words such as 'a' and 'the' omitted. Besides the obvious terms such as 'nonintrusive', 'appliance', 'load', 'monitoring', 'energy' and 'disaggregation', other interesting terms pop up, such as 'smart', 'identification', 'residential' and 'home'.

Update 12.09.2016: cleaned up graph appearance.

Update 12.09.2016: added data and notebook used to generate these graphs to the nilm-papers github repository.

GridCarbon app updated to Android 5

Yesterday I pushed v2.1 of the GridCarbon app to the Google Play store. GridCarbon is an Android and iOS app that lets you track the carbon intensity of the UK electricity grid on your smartphone or tablet.

This latest release includes the following changes:

• Updated to Android 5 material design
• Improved robustness when generation mix data received from server contains missing or repeated elements
• New about page

The app also allows you track carbon intensity and generation sources over the past 24 hours via the landscape view.

If you've you have any comments, I'd love to hear them!