Friday, 15 December 2017

NILM 2018 Conference Announced

Message from NILM 2018 organisers:

Dear NILM Researchers,

The dates for the 4th International Workshop on Non-Intrusive Load Monitoring (NILM) have been set. The event will take place on March 7th and 8th, 2018 in Austin, TX.
Papers for the workshop are due on December 29th, 2017, and can be submitted via the following site:

https://cmt3.research.microsoft.com/NILM2018

We encourage authors to submit 4-page papers using the IEEE Style,  on research that is ongoing  or contains recent results.
Note: because the workshop will only include online proceedings, submission to the workshop would not generally prevent any material from being submitted later to a journal or conference.
For more up-to-date news about the workshop, please visit our website.

Some important dates:

  • Paper Submission Deadline: December 29, 2017 (at 5pm EST)
  • Notification of Acceptance:  January 26, 2018
  • Final Paper Submission Due: February 5, 2018
  • Registration Deadline: February 24, 2018
  • Workshop: March 7-8, 2018

We look forward to receiving your paper submissions!

Regards,

Mario Bergés, Stephen Makonin and David Irwin
Workshop Organizing Committee

Tuesday, 5 September 2017

Dates for the 2017 EU NILM Workshop announced!


We’re pleased to announce that the fourth European Workshop on Non-intrusive Load Monitoring will be held on the 6-7th November 2017 in London, UK. The aim of the European NILM conference series is to bring together all of the European researchers that are working on the topic of energy disaggregation in both industry and academia. See http://www.nilm.eu/nilm-workshop-2017 for full details and to register your place.

Important dates


Early registration deadline: 20 September 2017 (free)
Final registration deadline: 20 October 2017 (£50)
Presentation abstract submission: 20th September 2017
Workshop dates: 6-7 November 2017

Call for presentations


We invite attendees to submit presentation abstracts via this Google Form by 20 September 2017. We will aim to build a balanced agenda from a combination of invited talks and submitted abstracts, while the remaining submissions will be invited to present a lightning talk and a poster. Since the workshop will not feature published proceedings, we encourage relevant submissions which have previously appeared at other venues. We also welcome submissions from companies with results or data which they are willing to share with the community.

Call for sponsors


We have a number of sponsorship options for the workshop, including opportunities to give a full presentation, present a demo, and exposure on the website and slides. We need sponsorship in order to provide lunch, refreshments and an evening reception, so please contact us if you’re interested!

We look forward to welcoming you in London!

Tuesday, 4 July 2017

SustainIT 2017 final call for papers

The SustainIT 2017 conference will be held December 6-7, in Funchal, Portugal, and the organisers have specifically encouraged submissions from the field of NILM. While the deadline for registering abstracts is the 7th July (!), full papers aren't required to be submitted until the 21st July.

An interesting point of this conference is that proceedings will be uploaded to IEEE Xplore and IFIP DL, but copyrights will remain with the authors. The organisers believe that keeping the copyrights with the authors is quite important, not only because of respecting the most recent EU policies on Open Access publications, but also because this allows the authors to submit extended versions of their papers to journals of their choice.

The full call for participation can be found at the link below:
https://sustainit2017.m-iti.org/participation/

Saturday, 27 May 2017

GridCarbon app updated to include solar

One of the benefits of appliance-level disaggregation is the potential to provide deferral suggestions, e.g. consider running the dishwasher later in the day. Such deferral suggestions are typically motivated by either cost (in the form of time-of-use tariffs) or carbon emissions (different power stations are active at different times of day). To help visualise the latter, Alex Rogers (my PhD supervisor) and I released the GridCarbon app, which tracks the carbon intensity of the UK electricity grid in real time.


Over the years, the generation mix of UK electricity has changed substantially, with a large amount of un-metered wind and solar generation appearing on the distribution network. We recently updated both the iOS and Android apps to reflect these changes, using generation estimates of wind and solar provided by ELEXON. Yesterday saw a solar generation record, with 8.7 GW of UK electricity provided by solar at its peak, although we can see from the app that this peak was short-lived:


GridCarbon primarily functions as an educational tool, and aims to visualise and communicate the current status of the UK electricity grid. Our goal is to make this information easily accessible so that it becomes common knowledge, and encourages informed debate about the future of our electricity infrastructure.

Monday, 10 April 2017

Jack's NILM Competition Survey

Jack Kelly recently published the results of a survey which he designed to assess the appetite for a NILM competition. The survey covers a range of topics, from technical questions regarding the sample rate and required features, to practicalities such as where algorithms should run and how often the competition should take place. However, the survey highlights two key issues that make the design of such a competition quite tricky:

Data - As Jack explained in a recent blog post, the collection of a large enough data set is expensive and there is no clear business case for a single organisation to pick up the cost. This is due to the sheer number of sensors per house, duration of data collection and number of houses required. Furthermore, the data set cannot be reused once the data set has been released or even once the accuracy of successive runs of an algorithm have been made available.

Requirements - Almost by definition, every disaggregation company and researcher is tackling the problem from a slightly different angle. Some use a unique sensor, while others require a unique training procedure. Even beyond the differences in disaggregation solutions, there's no clear consensus on issues such as where the competition should run or what training data should be provided.

However, all hope is not lost, given Pecan Street's demonstration that collecting sub-metered data at scale is possible, and also the precedent set by Belkin's competition back in 2013. Furthermore, most participants agreed on a few issues, such as 1 Hz active power being a reasonable place to start.

Jack's post-doc has now come to an end, which means he won't continue working towards running such a competition. If you fancy picking up the challenge, I'd encourage you to put a post on the google group and get involved!

Tuesday, 3 January 2017

COOLL dataset released

The COOLL dataset was recently released by researchers at the PRISME laboratory at the University of Orléans, which contains high-frequency from 12 different types of appliances. Similar to the tracebase and PLAID datasets, multiple instances of the each type were measured, and each instance was measured throughout 20 operations. During each controlled operation, current and voltage data was collected at a sample rate of 100 kHz. The dataset is summarised in an academic paper, and can be downloaded from github after filling in a registration form.

Friday, 4 November 2016

Energy Futures Lab talk at Imperial College London

I gave a talk at the Energy Futures Lab at Imperial College London this afternoon, which covered some of the data products which my team at Connected Home is responsible for providing to the rest of the business. Below you can find a summary of my talk:

Smart meters will be installed in 26 million UK homes over the next few years in an effort to achieve the country’s carbon emission reduction targets. Such smart meters will conform to the SMETS2 specification, which allows customers to chose whether to upload daily or half-hourly data to their supplier over the cellular network for billing purposes.

At Centrica Connected Home, we developed the My Energy dashboard for British Gas. This dashboard aims to not only visualise energy consumption, but also to extract meaningful insight from the consumption data. The dashboard offers a comparison of the customer’s consumption against similar homes, and also a monthly breakdown of their consumption into six categories; heating, hot water, lighting, entertainment, cooking and other appliances.

For the similar homes comparison, we rephrased this problem as the following question: “can we predict daily consumption given only the customer’s location and answers to a short survey?” We then built an algorithm to answer this question, and optimised the accuracy of this prediction given the huge dataset of all our customers’ actual consumptions. However, we realised that it was also important to balance single-day accuracy against the day-to-day stability of a single customer’s predicted consumption.

For the energy breakdown, we consider the problem again as a prediction problem, in which individual blocks of energy are detected from half hourly data and assigned to one of the six categories based on a range of features, such as magnitude, duration and time of day. We then optimised the accuracy of the algorithm using data collected from the Household Electricity Survey, which measured the consumption of individual appliances in addition to the total household’s consumption.

In addition to My Energy, Connected Home is probably best known for developing the Hive ecosystem of products, including Active Heating, Lighting, Motion Sensors, Door & Window Sensors, Smart Plugs and Boiler IQ. I’ve chosen to focus on Hive Active Heating in the rest of this talk, given that it’s the product that I’ve spent most time working on.

Hive Active Heating is a connected thermostat that allows customers to control their heating from their phone. However, Hive doesn’t instrument the boiler directly, but instead sends control signals to the boiler based on the ambient temperature of the home and the customer’s desired temperature. We’ve recently been experimenting with the possibility of detecting boiler failure from this limited set of features. Such a failure might consist of Hive asking the boiler to heat the home, followed by a decrease in the ambient temperature (rather than the expected increase in temperature). While this algorithm is still in its early stages of development, it illustrates a clear possibility to turn a connected product into a truly smart device.

In conclusion, I believe that smart meters offer huge potential to give customers insight into their energy consumption. Furthermore, I see real potential in the Internet of Things market, not only in connecting everyday appliances to the Internet, but also by enabling the insight and automated control which transforms them into smart appliances.