Disaggregated Homes

My name is Oliver Parson, and I'm currently employed as a Product Data Science Lead at Octopus. 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.

Tuesday, 5 September 2017

Dates for the 2017 EU NILM Workshop announced!

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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 L...
1 comment:
Tuesday, 4 July 2017

SustainIT 2017 final call for papers

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The SustainIT 2017 conference  will be held December 6-7, in Funchal, Portugal, and the organisers have specifically encouraged submissions ...
Saturday, 27 May 2017

GridCarbon app updated to include solar

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One of the benefits of appliance-level disaggregation is the potential to provide deferral suggestions, e.g. consider running the dishwasher...
Monday, 10 April 2017

Jack's NILM Competition Survey

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Jack Kelly  recently published the results  of a survey which he designed to assess the appetite for a NILM competition. The survey covers ...
2 comments:
Tuesday, 3 January 2017

COOLL dataset released

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The COOLL dataset was recently released by researchers at the PRISME laboratory at the University of Orléans, which contains high-frequency...
3 comments:
Friday, 4 November 2016

Energy Futures Lab talk at Imperial College London

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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...
Monday, 31 October 2016

Machine learning & the connected home: useful data or meaningless noise?

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Later this week I'll be giving a talk at the Energy Futures Lab on the recent work we've been doing around the data collected from...
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