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!

Collaboration with José Alcalá on using NILM for health monitoring

José has recently been visiting our lab in Southampton as part his PhD programme at the University of Alcalá. He's interested in how energy disaggregation can be applied within the healthcare domain, specifically to support elderly people living independently in their own homes. I've really enjoyed the collaboration so far, with José making great use of the data sets we've collected over the past few years. Despite only arriving in September last year, we've achieved a lot and recently co-authored a paper together. I hope José has found the visit useful, with him gaining experience from other members of our lab way beyond the domain of energy disaggregation, in fields such as Gaussian processes.

If you're also studying towards a PhD within the energy disaggregation domain and are interested in visiting our lab as part of your programme, please get in touch!

British Gas to acquire AlertMe

Today, both British Gas and AlertMe pushed out press releases announcing the acquisition of AlertMe by British Gas, for an estimated total of £65m. As a fresh member of British Gas' connected homes team and a long-term customer of AlertMe, I was really excited to hear the news and am eager to be part of the team to build the next generation of domestic energy feedback products. This move makes British Gas' position clear within the Internet of things / connected homes market, and will undoubtedly have a big impact on the field on energy disaggregation in the UK.

NILM 2015 @ London discussion

Jack Kelly and I are thinking about organising another NILM meet up in London this summer, and would love to get your thoughts on what you would find most useful. Jack has started an Energy Disaggregation Discussion Group which seems like a great place to throw some ideas around. I'd encourage everyone to head over to the forum and add your voice to the discussion! And don't forget to subscribe to the group so you receive updates!

DECC Workshop: Specifying and Costing Monitoring Equipment for a Longitudinal Energy Study

Yesterday I attended a DECC workshop aiming to specify and estimate the costs of monitoring equipment for a longitudinal energy study. The scale of the study would be to use questionnaires and monitoring equipment to study energy use in 10,000s of homes. However, only £500 per household could be spent on hardware at such scale. It was concluded that this budget would not go very far beyond aggregate gas, electricity and water monitoring equipment.

In contrast to this, the LUKES project proposes aggregate monitoring of 10,000s of homes, while extensively monitoring up to 400 homes for up to 4 years. Although not the primary aim of the study, such a data set would have considerable impact for the energy disaggregation community. I was keen to point out that such uses have arisen due to the Household Energy Study, and it is important to take this data set as a case study when designing new surveys.

However, it is also important that lessons are learned from HES, such that the same mistakes are not repeated. In particular, I’d hope that a new data set would:

• Collect both aggregate-level and circuit-level data as well as appliance level data
• Specify and maintain each household’s metering hierarchy and appliance names using a consistent metadata schema, such as the NILM Metadata project
• Collect aggregate data at a higher resolution than 2 minute energy readings. Ideally, I believe 1 second power data would be best trade-off between cost and frequency