Table 7 Clustering goals and decision rules for driving & load profiles of electric vehicles

The number of clusters should be as low as possible.

Since the resulting clusters are the basis for a subsequent optimization with high computation time, a lower number is favored.

max(I_parsimony)

Clusters should be relatively even in size.

The resulting representative driving & load profiles will be distributed according to their cluster size. If single clusters are overrepresented due to their size, the same driving & load profiles will be used and hence the desired variance will be low.

max(I_entropy)

Members of a cluster should be well represented by a specific datapoint within the dataset.

This is necessary in order to a) simulate driving & load profiles and b) have it be as similar to other points in the cluster as possible. Input features are a lower dimensional representation of driving & load profiles.

max(I_cp2cent)

Clusters should be describable by a low number of features.

Next to having unique and distinguishable characteristics, in order to create understandable “personas”, the number of characterizing features should be as low as possible.

max(I_pps)