  Energy companies often implement various demand response (DR) programs to
better match electricity demand and supply by offering the consumers incentives
to reduce their demand during critical periods. Classifying clients according
to their consumption patterns enables targeting specific groups of consumers
for DR. Traditional clustering algorithms use standard distance measurement to
find the distance between two points. The results produced by clustering
algorithms such as K-means, K-medoids, and Gaussian Mixture Models depend on
the clustering parameters or initial clusters. In contrast, our methodology
uses a shape-based approach that combines Agglomerative Hierarchical Clustering
(AHC) with Dynamic Time Warping (DTW) to classify residential households' daily
load curves based on their consumption patterns. While DTW seeks the optimal
alignment between two load curves, AHC provides a realistic initial clusters
center. In this paper, we compare the results with other clustering algorithms
such as K-means, K-medoids, and GMM using different distance measures, and we
show that AHC using DTW outperformed other clustering algorithms and needed
fewer clusters.
