My name is Oliver Parson, and I'm currently employed as a Senior Data Scientist at Bulb. I'm interested in investigating the ways in which machine learning can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NILM) or energy disaggregation.
The NILM 2020 Workshop will be a one day online conference held during the week commencing 16 November 2020. The workshop will be jointly organised by the International and European NILM Workshop teams, and will be free to attend. For the first time, the event will appear as a workshop of the larger ACM BuildSys conference, and accepted papers will be published in the ACM digital library. The paper submission deadline is 21 August, although abstracts should be registered by 14 August. Further details (including the conference date, registration and paper submission) will appear on the NILM 2020 Workshop website as they are finalised.
For context, this workshop represents the culmination of the 5th International NILM Workshop and the 7th European Workshop, as shown by the table below.
The DEBS Grand Challenge is a series of competitions in which both academics and professionals compete with the goal of building faster and more accurate distributed and event-based system. This year, the DEBS Grand Challenge focuses on NILM, with the goal of detecting when appliances contributing to an aggregated stream of voltage and current readings from a smart meter are switched on or off. The track has already closed to submissions, but the submissions will be presented at the DEBS 2020 conference, which will be held on Thursday 16th July. The whole conference is virtual and registration is free for participants. Registrants have access to the 5 day online program, including tutorials, keynotes, and main conference presentations, as well as access to the Slack discussion channel. The papers can also be downloaded via the online programme.
The IDEAL Household Energy Dataset comprises data from 255 UK homes. Alongside electric and gas data from each home the corpus contains individual room temperature and humidity readings and temperature readings from the boiler. For 39 of the 255 homes more detailed data is available, including individual electrical appliance use data, and data on individual radiators. Sensor data is augmented by anonymised survey data and metadata including occupant demographics, self-reported energy awareness and attitudes, and building, room and appliance characteristics.
On another note, George Hart also gave the keynote presentation at the 2016 International NILM Workshop in Vancouver, where he talked about NILM in the 1980s, and his more recent interests in sculpture and education.