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.

Thursday, 29 January 2015

NILM 2015 @ London discussion

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Jack Kelly and I are thinking about organising another NILM meet up in London this summer, and would love to get your thoughts on what you ...
Tuesday, 27 January 2015

DECC Workshop: Specifying and Costing Monitoring Equipment for a Longitudinal Energy Study

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Yesterday I attended a DECC workshop aiming to specify and estimate the costs of monitoring equipment for a longitudinal energy study. The s...
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Friday, 16 January 2015

Joining the data science team at British Gas Connected Homes

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I joined the data science team at British Gas Connected Homes at the start of this year, who focus on extracting meaningful insight from sma...
Monday, 5 January 2015

JAIR paper published: A Hidden Markov Model-Based Acoustic Cicada Detector for Crowdsourced Smartphone Biodiversity Monitoring

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We recently had a paper published in the Journal for Artificial Intelligence Research based on our work searching for the New Forest Cicada ...
Friday, 19 December 2014

infer-hmm: An Infer.NET implementation of the hidden Markov model

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The hidden Markov model is a popular statistical tool for modelling sequential data, and as such has  received much   attention from the f...
Tuesday, 25 November 2014

NILMTK Survey

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We've seen some really encouraging adoption of NILMTK  (our open-source toolkit for non-intrusive load monitoring) since we started work...
Monday, 24 November 2014

Energy disaggregation bloggers

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There's a great amount of blogging going on in the NILM research field, so this post aims to collect all these resources together in a s...
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