My name is Oliver Parson, and I'm currently employed as a Data Scientist at Centrica Connected Home. 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 (NIALM) or energy disaggregation.
Wednesday, 16 February 2011
Pattern matching by maximising smoothness
I've recently been trying a new approach to detecting an appliance's signature. The approach consists of:
Generating a typical appliance cycle
For each possible location of the cycle:
Subtract the typical appliance cycle from the aggregate readings
Calculate the smoothness of the resulting readings
The smoothness function I have used calculates the sum of the absolute values of the changes in power for the whole day.
Below are two plots of the smoothness function for each possible cycle location in the day. The graphs represent the smoothness function for two slightly different typical cycles.
We can see that both graphs identify the overnight fridge cycles well. However, both graphs also generate a large number of false positives. There's such a large number of of these that they obscure any correct positives during the day.