 clustering techniques are commonly used to group similar objects together from a large dataset. However, when dealing with time series data, such as building energy consumption patterns, it can be difficult to determine which similarity metrics should be used. This paper examines the effectiveness of different similarity metrics in clustering time series data, and proposes a new method called clustered vector balance to validate the performance of different clustering algorithms. This article was authored by Wolfgang Kastner and Felix Iglesias.