My name is Oliver Parson, and I'm currently employed as a Senior Data Scientist at Bulb. 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, 19 April 2013
New data set released by Pecan Street Research Institute
Pecan Street Research Institute recently announced the release of a new data set designed specifically to enable the evaluation of electricity disaggregation technology. A free sample data set is available to members of its research consortium, which has now been opened up to university researchers. The sample data set contains 7 days of data from 10 houses in Austin, TX, USA, for which both aggregate and circuit data is also available containing power readings at 1 minute intervals. In addition to common household loads, 2 of the houses also have photovoltaic systems and 1 house also has an electric vehicle.
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