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.

Monday, 21 September 2015

Energy disaggregation for health monitoring

›
José and I have been working on a project to apply NILM methods to the health monitoring domain, specifically to help monitor the activity o...
Tuesday, 8 September 2015

Dataport data released in NILMTK format

›
The Dataport database is the world's largest source of disaggregated customer energy data. The database contains electricity data colle...
Thursday, 13 August 2015

Reusable hold out test sets for NILM

›
Overfitting is a well-cited problem in the field of Non-intrusive Load Monitoring. Overfitting refers to the high accuracy of an algorithm o...
Tuesday, 28 July 2015

NILM 2015 Workshop Summary

›
During July 2015, Imperial College London played host to nearly 70 attendees from all over the world for the European Non-Intrusive Load...
Monday, 13 July 2015

NILM 2015 presentation videos

›
In case you missed the live stream of the Second European NILM Workshop, we've also uploaded each talk to a YouTube playlist . Oliv...
Saturday, 4 July 2015

NILM 2015 Live Stream

›
As the upcoming  European NILM Workshop  is now fully booked, we're also hoping to stream the presentation sessions via a Hangout On Air...
Saturday, 9 May 2015

What even is supervised/unsupervised disaggregation?

›
I've noticed a fair amount of disagreement regarding exactly what type of learning is being used by a specific energy disaggregation met...
2 comments:
‹
›
Home
View web version
Powered by Blogger.