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.
Tuesday, 3 January 2017
COOLL dataset released
The COOLL dataset was recently released by researchers at the PRISME laboratory at the University of Orléans, which contains high-frequency from 12 different types of appliances. Similar to the tracebase and PLAID datasets, multiple instances of the each type were measured, and each instance was measured throughout 20 operations. During each controlled operation, current and voltage data was collected at a sample rate of 100 kHz. The dataset is summarised in an academic paper, and can be downloaded from github after filling in a registration form.
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