Enhancing the clustering process in the category model load profiling

Load profiling based consumer categorisation is a procedure that involves stages such as the proper representation of the load data, the selection of one or more pattern recognition algorithms, the assessment of their performance, the formation of daily load curve clusters for each consumer and the formation of the final consumer clusters. This study proposes improvements for every stage, while a new stage is introduced to enhance the whole procedure. All the pattern representation techniques of the load profiling related literature are examined together with the most widely used clustering algorithm, namely the K-means. Two novel modified forms of the algorithm are introduced, specially designed by the authors for the problem under study. The aim is to optimally group together similar daily demand patterns to provide exploitable information to the utilities and the retailers for the efficient design and implementation of innovative energy services, targeted to the different consumer categories.