Data size reduction with symbolic aggregate approximation for electrical load pattern grouping

Abstract

Data size reduction techniques may be helpful in the process of categorising the electrical load consumption patterns on the basis of their shape. Starting from a macro-class of consumers defined according to certain general criteria on the type of consumers and the period of the year or week, the representative load pattern (RLP) of each consumer can be built by considering the data points in the time domain or a reduced number of features. This study exploits the effects of using the symbolic aggregate approximation (SAX) method to form the reduced set of features. The portion of the time–amplitude plane defining the RLP is partitioned into sub-portions on the basis of the characteristics of the whole data set. A specific partitioning of the time axis is proposed on the basis of the cumulative distribution function of the RLP variations in time. Each RLP is then coded according to the SAX principles. The resulting codes are then used into a hierarchical clustering procedure. The validity of the clustering results obtained by using the SAX data representation with the proposed non-uniform partitioning of the time axis is presented and discussed, including comparisons with the results obtained from other data size reduction techniques.