The global demand for energy has increased in recent years. In Northern Europe and North America, centralized production and distribution of heat energy is commonly regarded as District Heating (DH). Efficient delivery of heat in the DH system is crucial not only for the building dwellers but even for companies that supply such energy. DH efficiency has to overcome several challenges as a result of faults that negatively impact its performance. Data collected from substations can be analyzed to identify potential faults and reduce the associated economic costs. The aim of this study is to use unsupervised machine learning in order to identify potential clusters of buildings in a time series dataset collected from buildings in a medium size Swedish town. We propose to find the anomalies in two ways; firstly, by identifying possible clusters of buildings and finding buildings which do not belong to a cluster, that can constitute potential anomalies. Secondly, by studying how the cluster membership transitions can help us to identify abnormal behavior over different time windows. A data mining experiment has been conducted by analyzing the energy profiles of 90 buildings in a period of 8 weeks for 2017 using the DBSCAN algorithm. Results suggest that winter period is more appropriate for the formation of possible clusters compared to summer period due to less noise encountered in winter. Clustering for every week can tell us more about the anomalies. Last, the periodic transitions between the clusters and the ranking of the clusters based on scaled distance can help us improve the anomaly detection by signalizing us for further inspection.