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.

Friday, 9 September 2011

Top 10 papers for Non-Intrusive Appliance Load Monitoring (NIALM) - updated for 2011

›
Update: Top papers of 2012 About a year ago I posted a short list of some of the most useful papers for NIALM . Since then I have learnt a...
8 comments:
Wednesday, 7 September 2011

The Reference Energy Disaggregation Data Set

›
Accurately comparing the performance of energy disaggregation (Non-intrusive Appliance Load Monitoring) methods is almost impossible unless ...
4 comments:
Friday, 1 July 2011

Plan for refinement of appliance model

›
At this stage, there are three potential directions I would like to explore to improve our current model of appliances: Use of additional ...
Thursday, 30 June 2011

9 Month Report: Using Hidden Markov Models for Non-intrusive Appliance Load Monitoring

›
Today I submitted my 9 month report : Title: Using Hidden Markov Models for Non-intrusive Appliance Load Monitoring Abstract: With the g...
Wednesday, 29 June 2011

Using a single hidden Markov model to model multiple appliances

›
Previous posts have shown how a single appliance's states can be determined from its power readings using a hidden Markov model. This po...
Wednesday, 1 June 2011

Calculating the optimal appliance state transitions for an observed data set - continued

›
This post extends the previous post, by applying the same technique to a multi-state appliance. The classification accuracy was tested using...
3 comments:
Thursday, 26 May 2011

Calculating the optimal appliance state transitions for an observed data set

›
As explained in previous posts, we can model the conditional probabilities of appliance states and aggregate power readings using a Markov c...
‹
›
Home
View web version
Powered by Blogger.