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.
Monday, 10 January 2011
Summary of appliance signatures and plan for this week
We can account a third of Alex's energy consumption to the tumble dryer, dishwasher, washing machine and 'always on' appliances.
The signatures generated by individual appliances contain the following distinguishing features:
Alternating/constant power draw
Shape of energy consumption
Although signatures containing an alternating power draw are not identical, this is still a useful feature to identify a signature by. This might still be visible even amongst the noise generated by overlapping energy draws from other appliances.
The reason why the shape of the energy consumption should be used instead of the power consumption is due to the repeatability of appliance signatures. This is due to some appliances drawing an alternating power with a frequency different to the sampling frequency.
However, the shape of the energy consumption only works well when appliances have a fixed length. Such appliances account for approximately a third of total energy consumption. Therefore this feature is only applicable to categorise the minority of energy consumption.
In addition, when appliances overlap this shape matching approach corresponds to picking the combination of signatures which minimises the error. However, this approach is not robust to noise generated from other appliances which do not have a fixed length.
As a first step towards shape matching, it is beneficial to subtract the consumption of all appliances known to be on from the aggregate consumption. This will be my main aim of work this week.