A new pattern recognition methodology for classification of load profiles for ships electric consumers

Abstract

In this paper a new pattern recognition methodology is presented for the classification of the daily chronological load curves of ship electric consumers (equipment) and the determination of the respective typical load curves of each one of them. It is based on pattern recognition methods, such as k-means, adaptive vector quantisation, fuzzy k-means, self-organising maps and hierarchical clustering, which are theoretically described and properly adapted. The parameters of each clustering method are properly selected by an optimisation process, which is separately applied for each one of six adequacy measures: the error function, the mean index adequacy, the clustering dispersion indicator, the similarity matrix indicator, the Davies-Bouldin indicator and the ratio of within cluster sum of squares to between cluster variation. As a study case, this methodology is applied to a set of consumers of Hellenic Navy MEKO type frigates.