Data generated by industrial systems such as District Heating (DH) often lack meaningful labels for supervised Machine Learning (ML) methods in anomaly detection. Consequently, unsupervised and semi-supervised ML methods are widely used. These methods frequently uncover numerous anomalies, necessitating labor-intensive post-processing. This paper proposes an algorithm to detect topK anomaly instances with similar patterns (energy signatures) to known anomalies, and to identify clusters of similar anomalies using hierarchical clustering. Similarities between anomaly instances are computed using Dynamic Time Warping and Matrix Profiles. Generative Adversarial Networks (GANs) are employed to augment small anomaly datasets. Results demonstrate the effectiveness of the proposed algorithm in reducing the manual effort required for post-processing anomalies in a DH dataset.