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 31 March 2015
EEme release disaggregation accuracy calculated by Pecan Street
I recently came across EEme, a spin-out from Carnegie Mellon University, who apply energy disaggregation to 15-minute smart meter data to provide demand side management analytics. EEme are particularly interesting, since they’re the first company (to the best of my knowledge) to go completely public regarding the accuracy of their product, as calculated by a third party. EEme used Pecan Street’s 3rd party evaluation tool, which provided EEme with 15-minute aggregate smart meter data and weather data from hundreds of homes, and required EEme to return monthly totals for four of the largest energy consuming appliances. Since Pecan Street also measured the appliance-level energy consumption as part of their deployment, they are able to calculate the exact level of accuracy of EEme’s disaggregation. The full 7 page report is available for request from EEme’s website. Although EEme focus on lower resolution data than many other energy disaggregation companies, I’m interested to see whether this report sparks the beginning of a more public competition between energy disaggregation companies to release accuracy statistics as confirmed by a third party.
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