  A major challenge of using AMI data in power system analysis is the large
size of the data sets. For rapid analysis that addresses historical behavior of
systems consisting of a few hundred feeders, all of the AMI load data can be
loaded into memory and used in a power flow analysis. However, if a system
contains thousands of feeders then the handling of the AMI data in the analysis
becomes more challenging. The work here seeks to demonstrate that the
information contained in large AMI data sets can be compressed into accurate
load models using wavelets. Two types of wavelet based load models are
considered, the multi-resolution wavelet load model for each individual
customer and the classified wavelet load model for customers that share similar
load patterns. The multi-resolution wavelet load model compresses the data, and
the classified wavelet load model further compresses the data. The method of
grouping customers into classes using the wavelet based classification
technique is illustrated.
