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Driving innovation in the energy sector. Cutting-edge AI connecting you to your home. We are a London-based team of Data Scientists and Machine Learning experts specialising in high-frequency disaggregation and data analytics in the energy sector. Smart Meters are being rolled out across hom.
Thursday, July 30, 2015. We recently announced the release of the stable version 0.
My name is Oliver Parson, and I currently divide my time between my research fellowship at the University of Southampton and the Connected Homes team at British Gas. Thursday, 13 August 2015. Reusable hold out test sets for NILM. An MSc group from Imperia.
I currently divide my time between my research fellowship in the Agents, Interaction and Complexity Group. At the University of Southampton. And the Connected Homes team at British Gas. Specifically, my research focuses on the ways in which machine learning techniques can be used to break down household energy consumption data into individual appliances.
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The UKs Non Intrusive Load Monitoring NILM networkDESCRIPTION
The aim of this site NILM Workshop is to bring together researchers that are working on the topic of energy disaggregation in both academia industry.PARSED CONTENT
The site nilm.eu states the following, "The 2015 NILM Workshop is now closed." I noticed that the webpage also stated " Thank you to all attendees, speakers and organisers for." They also stated " Helping to make it such a successful day! You can view the presentations again by following the link below. During the next week or so we will break the presentations into separate videos and share slides and posters with you. NILM stands for Non Intrusive Load Monitoring. This is the process which disaggregates your total energy consumption into the energy readings of an appliance ."SEE MORE DOMAINS
New features in nilmtk disaggregation methods. This blog post introduces some of the added features in nilmtk. The ability to choose the optimal number of . Finding original building id in nilmtk.
I currently divide my time between my research fellowship in the Agents, Interaction and Complexity Group. At the University of Southampton. And the Connected Homes team at British Gas. Specifically, my research focuses on the ways in which machine learning techniques can be used to break down household energy consumption data into individual appliances.
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