In this paper, we perform a cluster analysis using smart meter electricity demand data from 656 households in Switzerland, collected during one year. First, we present the silhouette analysis to determine the optimum number of clusters for a k-means clustering approach. Secondly, we try different distance functions used in the k-means clustering to partition the samples into different categories. We find that the choice of distance function has no effect on the clustering performance. Finally, we investigate the "dimensionality curse" and find that low dimensions should be preferred to increase the quality of the clustering outcome.