Dr Omid R.E. Motlagh1, Dr Adam Berry, Mr Lachlan O’Neil
1CSIRO-Energy , Mayfield West, Australia
To better understand the patterns of residential electricity load behaviour, it is often useful to group similarly behaving homes together. Most contemporary clustering methods that would otherwise be well suited to the task, however, perform poorly on the type of noisy and patchy time-series datasets common to the residential energy domain. They are also limited by a strong preference for time-series data that is synchronised: of both the same extent and sampling frequency. We describe a model-based load-clustering method to address the problems of unequal and asynchronous time series as well as lack of robustness against input noise. The clustering results – using a large dataset of 7000 homes – show around 14% improvement of coincidence factor averaged across the obtained clusters versus the full set of the homes. The method also results in around 73% similarity between clusters using synchronous and asynchronous-unequal profiles. It also shows high robustness of more than 90% against a heavy mixed noise with signal-to-noise ratio as low as 29%. There are many applications of this load clustering technique including handling incomplete and noisy datasets, some of which are exemplified in this presentation.
Omid Motlagh is a research scientist with the CSIRO-Energy based in Newcastle. His project-research interest is Energy, Urban and Systems Engineering, Machine Learning, and Experimental Mathematics. Adam Berry is the Grids and Energy Efficiency Program Research Group Leader and is the director of the Energy Use Data Model project for the Department of the Environment and Energy. Lachlan O’Neil is an Energy Data Statistician and Scientist for the Energy Use Data Model project for the Department of Environment and Energy.